289 results on '"Dumontier M"'
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2. List of Contributors
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Abbas, B., primary, Abreu, A., additional, Adams, R., additional, Adolfsson-Erici, M., additional, Afonso, A., additional, Afonso-Olivares, C., additional, Agirbas, E., additional, Aguiló, J.M., additional, Airoldi, L., additional, Aksoy, H., additional, Albentosa, M., additional, Alcaro, L., additional, Aliani, S., additional, Al-Maslamani, I., additional, Alomar, C., additional, Altin, D., additional, Álvarez, E., additional, Amaral-Zettler, L.A., additional, Amato, E., additional, Anderson, A., additional, Andrady, A.L., additional, Andrius, G., additional, Angel, D., additional, Ariese, F., additional, Arp, H.P., additional, Asensio, M., additional, Assidqi, K., additional, Avio, C.G., additional, Aytan, U., additional, Bahri, T., additional, Baini, M., additional, Bakir, A., additional, Ball, H., additional, Baranyi, C., additional, Barboza, L.G.A., additional, Barg, U., additional, Bargelloni, L., additional, Barras, H., additional, Barrera, C., additional, Barria, P., additional, Barrows, A., additional, Barth, A., additional, Batel, A., additional, Baztan, J., additional, Baztan, P., additional, Beiras, R., additional, Benedetti, M., additional, Berber, A.A., additional, Berber, N., additional, Bergmann, M., additional, Berlino, M., additional, Berrow, S., additional, Bessa, F., additional, Besseling, E., additional, Beyer, B., additional, Binaglia, M., additional, Bizjak, T., additional, Bjorndal, K.A., additional, Blust, R., additional, Boertien, M., additional, Bolten, A.B., additional, Booth, A.M., additional, Bounoua, B., additional, Bourseau, P., additional, Brahimi, N., additional, Bramini, M., additional, Brennholt, N., additional, Breuninger, E., additional, Bried, J., additional, Broderick, A., additional, Broglio, E., additional, Browne, M.A., additional, Bruzaud, S., additional, Buceta, J., additional, Buchinger, S., additional, Budimir, S., additional, Budzin-ski, H., additional, Butter, E., additional, Cachot, J., additional, Caetano, M., additional, Callaghan, A., additional, Camedda, A., additional, Capella, S., additional, Cardelli, L., additional, Carpentieri, S., additional, Carrasco, A., additional, Carriço, R., additional, Caruso, A., additional, Cassone, A.-L., additional, Castillo, A., additional, Castro, R.O., additional, Catarino, A.I., additional, Cazenave, P.W., additional, Çelik, İ., additional, Cerralbo, P., additional, César, G., additional, Chouinard, O., additional, Chubarenko, I., additional, Chubarenko, I.P., additional, Cicero, A.M., additional, Clarindo, G., additional, Clarke, B., additional, Clérandeau, C., additional, Clüsener-Godt, M., additional, Codina-García, M., additional, Cole, M., additional, Collard, F., additional, Collignon, A., additional, Collins, T., additional, Compa, M., additional, Conan, P., additional, Constant, M., additional, Cordier, M., additional, Courtene-Jones, W., additional, Cousin, X., additional, Covelo, P., additional, Cózar, A., additional, Crichton, E., additional, Crispi, O., additional, Cronin, M., additional, Croot, P.L., additional, Cruz, M.J., additional, d’Errico, G., additional, Dâmaso, C., additional, Das, K., additional, de Alencastro, L.F., additional, de Araujo, F.V., additional, de Boer, J.F., additional, de Lucia, G.A., additional, Debeljak, P., additional, Dehaut, A., additional, Deudero, S., additional, Devrieses, L., additional, Di Vito, S., additional, Díaz, A., additional, Donohue, J., additional, Doumenq, P., additional, Doyle, T.K., additional, Dris, R., additional, Druon, J.-N., additional, Duarte, C.M., additional, Duflos, G., additional, Dumontier, M., additional, Duncan, E., additional, Dussud, C., additional, Eckerlebe, A., additional, Egelkraut-Holtus, M., additional, Eidsvoll, D.P., additional, Ek, C., additional, Elena, S., additional, Elineau, A., additional, Enevoldsen, H., additional, Eppe, G., additional, Eriksen, M., additional, Ernsteins, R., additional, Espino, M., additional, Estévez-Calvar, N., additional, Ewins, C., additional, Fabre, P., additional, Faimali, M., additional, Fattorini, D., additional, Faure, F., additional, Ferrando, S., additional, Ferreira, J.C., additional, Ferreira-da-Costa, M., additional, Fileman, E., additional, Fischer, M., additional, Fortunato, A.B., additional, Fossi, M.C., additional, Foulon, V., additional, Frank, A., additional, Frenzel, M., additional, Frère, L., additional, Frias, J.P.G.L., additional, Frick, H., additional, Froneman, P.W., additional, Gabet, V.M., additional, Gabrielsen, G.W., additional, Gago, J., additional, Gajst, T., additional, Galgani, F., additional, Gallinari, M., additional, Galloway, T.S., additional, Gamarro, E.G., additional, Gambardella, C., additional, Garaventa, F., additional, Garcia, S., additional, Garrabou, J., additional, Garrido, P., additional, Gary, S.F., additional, Gasperi, J., additional, Gaze, W., additional, Geertz, T., additional, Gelado-Caballero, M.D., additional, George, M., additional, Gercken, J., additional, Gerdts, G., additional, Ghiglione, J.-F., additional, Gies, E., additional, Gilbert, B., additional, Giménez, L., additional, Glassom, D., additional, Glockzin, M., additional, Godley, B., additional, Goede, K., additional, Goksøyr, A., additional, Gómez, M., additional, Gómez-Parra, A., additional, González-Marco, D., additional, González-Solís, J., additional, Gorbi, S., additional, Gorokhova, E., additional, Gorsky, G., additional, Gosch, M., additional, Grose, J., additional, Guebitz, G.M., additional, Guedes-Alonso, R., additional, Guijarro, B., additional, Guilhermino, L., additional, Gundry, T., additional, Gutow, L., additional, Haave, M., additional, Haeckel, M., additional, Haernvall, K., additional, Hajbane, S., additional, Hamann, M., additional, Hämer, J., additional, Hamm, T., additional, Hansen, B.H., additional, Hardesty, B.D., additional, Harth, B., additional, Hartikainen, S., additional, Hassellöv, M., additional, Hatzky, S., additional, Healy, M.G., additional, Hégaret, H., additional, Henry, T.B., additional, Hermabessiere, L., additional, Hernández-Brito, J.J., additional, Hernandez-Gonzalez, A., additional, Hernandez-Milian, G., additional, Hernd, G., additional, Herrera, A., additional, Herring, C., additional, Herzke, D., additional, Heussner, S., additional, Hidalgo-Ruz, V., additional, Himber, C., additional, Holland, M., additional, Hong, N.-H., additional, Horton, A.A., additional, Horvat, P., additional, Huck, T., additional, Huhn, M., additional, Huvet, A., additional, Iglesias, M., additional, Igor, C., additional, Isachenko, I.A., additional, Ivar do Sul, J-A., additional, Jahnke, A., additional, Janis, B., additional, Janis, K., additional, Janis, U., additional, Jemec, A., additional, Jiménez, J.C., additional, Johnsen, H., additional, Jorgensen, B., additional, Jørgensen, J.H., additional, Jörundsdóttir, H., additional, Jung, Y.-J., additional, Kedzierski, M., additional, Keiter, S., additional, Kershaw, P., additional, Kerhervé, P., additional, Kesy, K., additional, Khan, F., additional, Khatmullina, L.I., additional, Kirby, J., additional, Kiriakoulakis, K., additional, Klein, R., additional, Klunderud, T., additional, Knudsen, C.M.H., additional, Knudsen, T.B., additional, Kochleus, C., additional, Koelmans, A.A., additional, Kögel, T., additional, Koistinen, A., additional, Kopke, K., additional, Korez, Š., additional, Kowalski, N., additional, Kreikemeyer, B., additional, Kroon, F., additional, Krumpen, T., additional, Krzan, A., additional, Kržan, A., additional, Labrenz, M., additional, Lacroix, C., additional, Ladirat, L., additional, Laforsch, C., additional, Lagarde, F., additional, Lahive, E., additional, Lambert, C., additional, Lapucci, C., additional, Lattin, G., additional, Law, K.L., additional, Le Roux, F., additional, Le Souef, K., additional, Le Tilly, V., additional, Lebreton, L., additional, Leemans, E., additional, Lehtiniemi, M., additional, Lenz, M., additional, Leskinen, J., additional, Leslie, H., additional, Leslie, H.A., additional, Levasseur, C., additional, Lewis, C., additional, Licandro, P., additional, Lind, K., additional, Lindeque, P., additional, Lindeque, P.K., additional, Lips, I., additional, Liria, A., additional, Liria-Loza, A., additional, Llinás, O., additional, Loiselle, S.A., additional, Long, M., additional, Lorenz, C., additional, Lorenzo, S.M., additional, Loubar, K., additional, Luna-Jorquera, G., additional, Lusher, A.L., additional, Macchia, V., additional, MacGabban, S., additional, Mackay, K., additional, MacLeod, M., additional, Maes, T., additional, Magaletti, E., additional, Maggiore, A., additional, Magnusson, K., additional, Mahon, A.M., additional, Makorič, P., additional, Mallow, O., additional, Marques, J., additional, Marsili, L., additional, Martí, E., additional, Martignac, M., additional, Martin, J., additional, Martínez, I., additional, Martínez, J., additional, Martinez-Gil, M., additional, Martins, H.R., additional, Matiddi, M., additional, Maximenko, N., additional, Mazlum, R., additional, Mcadam, R., additional, Mcknight, L., additional, McNeal, A.W., additional, Measures, J., additional, Mederos, M.S., additional, Mendoza, J., additional, Meyer, M.S., additional, Miguelez, A., additional, Milan, M., additional, Militão, T., additional, Miller, R.Z., additional, Mino-Vercellio-Verollet, M., additional, Mir, G., additional, Miranda-Urbina, D., additional, Misurale, F., additional, Montesdeoca-Esponda, S., additional, Mora, J., additional, Morgana, S., additional, Moriceau, B., additional, Morin, B., additional, Morley, A., additional, Morrison, L., additional, Murphy, F., additional, Naidoo, T., additional, Näkki, P., additional, Napper, I.E., additional, Narayanaswamy, B.E., additional, Nash, R., additional, Negri, A., additional, Nel, H.A., additional, Nerheim, M.S., additional, Nerland, I.L., additional, Neto, J., additional, Neves, V., additional, Nies, H., additional, Noel, M., additional, Nor, N.H.M., additional, Noren, F., additional, O’ Connell, B., additional, O’ Connor, I., additional, Obbard, J.P., additional, Oberbeckmann, S., additional, Obispo, R., additional, Officer, R., additional, Ogonowski, M., additional, Orbea, A., additional, Ortlieb, M., additional, Osborn, A.M., additional, Ostiategui-Francia, P., additional, Packard, T., additional, Pahl, S., additional, Palatinus, A., additional, Palmqvist, A., additional, Pannetier, P., additional, Panti, C., additional, Parmentier, E., additional, Pasanen, P., additional, Patarnello, T., additional, Pattiaratchi, C., additional, Pauletto, M., additional, Paulus, M., additional, Pavlekovsky, K., additional, Pedersen, H.B., additional, Pedrotti, M.-L., additional, Peeken, I., additional, Peeters, D., additional, Peeters, E., additional, Pellegrini, D., additional, Perales, J.A., additional, Perez, E., additional, Perz, V., additional, Petit, S., additional, Pflieger, M., additional, Pham, C.K., additional, Piazza, V., additional, Pinto, M., additional, Planells, O., additional, Plaza, M., additional, Pompini, O., additional, Potthoff, A., additional, Prades, L., additional, Primpke, S., additional, Proietti, M., additional, Proskurowski, G., additional, Puig, C., additional, Pujo-Pay, M., additional, Pullerits, K., additional, Queirós, A.M., additional, Quinn, B., additional, Raimonds, E., additional, Ramis-Pujol, J., additional, Rascher-Friesenhausen, R., additional, Reardon, E., additional, Regoli, F., additional, Reichardt, A.M., additional, Reifferscheid, G., additional, Reilly, K., additional, Reisser, J., additional, Riba, I., additional, Ribitsch, D., additional, Rinnert, E., additional, Rios, N., additional, Rist, S.E., additional, Rivadeneira, M.M., additional, Rivière, G., additional, Robbens, J., additional, Robertson, C.J.R., additional, Rocher, V., additional, Rochman, C.M., additional, Rodrigues, M., additional, Rodriguez, Y., additional, Rodríguez, A., additional, Rodríguez, G., additional, Rodríguez, J.R.B., additional, Rodríguez, S., additional, Rodríguez, Y., additional, Rogan, E., additional, Rojo-Nieto, E., additional, Romeo, T., additional, Ross, P.S., additional, Roveta, A., additional, Rowland, S.J., additional, Ruckstuhl, N.A., additional, Ruiz-Fernández, A-C., additional, Ruiz-Orejón, L.F., additional, Runge, J., additional, Russell, M., additional, Saavedra, C., additional, Saborowski, R., additional, Sahin, B.E., additional, Sailley, S., additional, Sakaguchi-Söder, K., additional, Salaverria, I., additional, Sánchez-Arcilla, A., additional, Sánchez-Nieva, J., additional, Sanderson, W., additional, Santana-Rodríguez, J.J., additional, Santana-Viera, S., additional, Santos, M.B., additional, Santos, M.R., additional, Sanz, M.R., additional, Sardá, R., additional, Savelli, H., additional, Schoeneich-Argent, R., additional, Scholz-Böttcher, B.M., additional, Sciacca, F., additional, Scofield, R.P., additional, Setälä, O., additional, Selenius, M., additional, Sempere, R., additional, Senturk, Y., additional, Shashoua, Y., additional, Sherman, P., additional, Sick, C., additional, Siegel, D., additional, Sierra, J.P., additional, Silva, F., additional, Silvestri, C., additional, Sintija, G., additional, Sire, O., additional, Slat, B., additional, Smit, A., additional, Sobral, P., additional, Sorvari, J., additional, Sosa-Ferrera, Z., additional, Sotillo, M.G., additional, Soudant, P., additional, Speidel, L., additional, Spurgeon, D.J., additional, Steer, M.K., additional, Steindal, C.C., additional, Stifanese, R., additional, Štindlová, A., additional, Stuurman, L., additional, Suaria, G., additional, Suazo, C.G., additional, Sureda, A., additional, Surette, C., additional, Svendsen, C., additional, Syberg, K., additional, Tairova, Z., additional, Talvitie, J., additional, Tassin, B., additional, Tazerout, M., additional, Tekman, M.B., additional, ter Halle, A., additional, Thiel, M., additional, Thomas, K.V., additional, Thompson, R.C., additional, Tinkara, T., additional, Tirelli, V., additional, Tomassetti, P., additional, Toorman, E., additional, Toppe, J., additional, Tornambè, A., additional, Torres, R., additional, Torres-Padrón, M.E., additional, Underwood, A.J., additional, Urbina, M., additional, Usategui-Martín, A., additional, Usta, R., additional, Valdés, L., additional, Valente, A., additional, Valentina, T., additional, van Arkel, K., additional, Van Colen, C., additional, Van Der Hal, N., additional, van Franeker, J.A., additional, Van Herwerden, L., additional, Van Loosdrecht, M., additional, van Oyen, A., additional, Vandeperre, F., additional, Vanderlinden, J-P., additional, Vani, D., additional, Vasconcelos, L., additional, Vega-Moreno, D., additional, Ventero, A., additional, Vethaak, A.D., additional, Vianello, A., additional, Vicioso, M., additional, Vieira, L.R., additional, Viršek, M.K., additional, Vos, M., additional, Wahl, M., additional, Wallace, N., additional, Walton, A., additional, Waniek, J.J., additional, Watts, A., additional, Webster, L., additional, Wesch, C., additional, Whitfield, E., additional, Wichels, A., additional, Wieczorek, A.M., additional, Wilcox, C., additional, Williams, R.J., additional, Wong-Wah-Chung, P., additional, Wright, S., additional, Wyles, K.J., additional, Young, R., additional, Yurtsever, M., additional, Yurtsever, U., additional, Zada, L., additional, Zamani, N.P., additional, and Zampetti, G., additional
- Published
- 2017
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3. Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science
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Unni, DR, Unni, DR, Moxon, SAT, Bada, M, Brush, M, Bruskiewich, R, Caufield, JH, Clemons, PA, Dancik, V, Dumontier, M, Fecho, K, Glusman, G, Hadlock, JJ, Harris, NL, Joshi, A, Putman, T, Qin, G, Ramsey, SA, Shefchek, KA, Solbrig, H, Soman, K, Thessen, AE, Haendel, MA, Bizon, C, Mungall, CJ, Acevedo, L, Ahalt, SC, Alden, J, Alkanaq, A, Amin, N, Avila, R, Balhoff, J, Baranzini, SE, Baumgartner, A, Baumgartner, W, Belhu, B, Brandes, M, Brandon, N, Burtt, N, Byrd, W, Callaghan, J, Cano, MA, Carrell, S, Celebi, R, Champion, J, Chen, Z, Chen, MJ, Chung, L, Cohen, K, Conlin, T, Corkill, D, Costanzo, M, Cox, S, Crouse, A, Crowder, C, Crumbley, ME, Dai, C, De Miranda Azevedo, R, Deutsch, E, Dougherty, J, Duby, MP, Duvvuri, V, Edwards, S, Emonet, V, Fehrmann, N, Flannick, J, Foksinska, AM, Gardner, V, Gatica, E, Glen, A, Goel, P, Gormley, J, Greyber, A, Haaland, P, Hanspers, K, He, K, Henrickson, J, Hinderer, EW, Hoatlin, M, Hoffman, A, Huang, S, Huang, C, Hubal, R, Huellas-Bruskiewicz, K, Huls, FB, Hunter, L, Hyde, G, Issabekova, T, Jarrell, M, Jenkins, L, Johs, A, Kang, J, Kanwar, R, Kebede, Y, Kim, KJ, Kluge, A, Knowles, M, Koesterer, R, Korn, D, Unni, DR, Unni, DR, Moxon, SAT, Bada, M, Brush, M, Bruskiewich, R, Caufield, JH, Clemons, PA, Dancik, V, Dumontier, M, Fecho, K, Glusman, G, Hadlock, JJ, Harris, NL, Joshi, A, Putman, T, Qin, G, Ramsey, SA, Shefchek, KA, Solbrig, H, Soman, K, Thessen, AE, Haendel, MA, Bizon, C, Mungall, CJ, Acevedo, L, Ahalt, SC, Alden, J, Alkanaq, A, Amin, N, Avila, R, Balhoff, J, Baranzini, SE, Baumgartner, A, Baumgartner, W, Belhu, B, Brandes, M, Brandon, N, Burtt, N, Byrd, W, Callaghan, J, Cano, MA, Carrell, S, Celebi, R, Champion, J, Chen, Z, Chen, MJ, Chung, L, Cohen, K, Conlin, T, Corkill, D, Costanzo, M, Cox, S, Crouse, A, Crowder, C, Crumbley, ME, Dai, C, De Miranda Azevedo, R, Deutsch, E, Dougherty, J, Duby, MP, Duvvuri, V, Edwards, S, Emonet, V, Fehrmann, N, Flannick, J, Foksinska, AM, Gardner, V, Gatica, E, Glen, A, Goel, P, Gormley, J, Greyber, A, Haaland, P, Hanspers, K, He, K, Henrickson, J, Hinderer, EW, Hoatlin, M, Hoffman, A, Huang, S, Huang, C, Hubal, R, Huellas-Bruskiewicz, K, Huls, FB, Hunter, L, Hyde, G, Issabekova, T, Jarrell, M, Jenkins, L, Johs, A, Kang, J, Kanwar, R, Kebede, Y, Kim, KJ, Kluge, A, Knowles, M, Koesterer, R, and Korn, D
- Abstract
Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.
- Published
- 2022
4. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
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Menden M, Wang D, Mason M, Szalai B, Bulusu K, Guan Y, Yu T, Kang J, Jeon M, Wolfinger R, Nguyen T, Zaslavskiy M, Jang I, Ghazoui Z, Ahsen M, Vogel R, Neto E, Norman T, Tang E, Garnett M, Di Veroli G, Fawell S, Stolovitzky G, Guinney J, Dry J, Saez-Rodriguez J, Abante J, Abecassis B, Aben N, Aghamirzaie D, Aittokallio T, Akhtari F, Al-lazikani B, Alam T, Allam A, Allen C, de Almeida M, Altarawy D, Alves V, Amadoz A, Anchang B, Antolin A, Ash J, Aznar V, Ba-alawi W, Bagheri M, Bajic V, Ball G, Ballester P, Baptista D, Bare C, Bateson M, Bender A, Bertrand D, Wijayawardena B, Boroevich K, Bosdriesz E, Bougouffa S, Bounova G, Brouwer T, Bryant B, Calaza M, Calderone A, Calza S, Capuzzi S, Carbonell-Caballero J, Carlin D, Carter H, Castagnoli L, Celebi R, Cesareni G, Chang H, Chen G, Chen H, Cheng L, Chernomoretz A, Chicco D, Cho K, Cho S, Choi D, Choi J, Choi K, Choi M, De Cock M, Coker E, Cortes-Ciriano I, Cserzo M, Cubuk C, Curtis C, Van Daele D, Dang C, Dijkstra T, Dopazo J, Draghici S, Drosou A, Dumontier M, Ehrhart F, Eid F, ElHefnawi M, Elmarakeby H, van Engelen B, Engin H, de Esch I, Evelo C, Falcao A, Farag S, Fernandez-Lozano C, Fisch K, Flobak A, Fornari C, Foroushani A, Fotso D, Fourches D, Friend S, Frigessi A, Gao F, Gao X, Gerold J, Gestraud P, Ghosh S, Gillberg J, Godoy-Lorite A, Godynyuk L, Godzik A, Goldenberg A, Gomez-Cabrero D, Gonen M, de Graaf C, Gray H, Grechkin M, Guimera R, Guney E, Haibe-Kains B, Han Y, Hase T, He D, He L, Heath L, Hellton K, Helmer-Citterich M, Hidalgo M, Hidru D, Hill S, Hochreiter S, Hong S, Hovig E, Hsueh Y, Hu Z, Huang J, Huang R, Hunyady L, Hwang J, Hwang T, Hwang W, Hwang Y, Isayev O, Walk O, Jack J, Jahandideh S, Ji J, Jo Y, Kamola P, Kanev G, Karacosta L, Karimi M, Kaski S, Kazanov M, Khamis A, Khan S, Kiani N, Kim A, Kim J, Kim K, Kim S, Kim Y, Kirk P, Kitano H, Klambauer G, Knowles D, Ko M, Kohn-Luque A, Kooistra A, Kuenemann M, Kuiper M, Kurz C, Kwon M, van Laarhoven T, Laegreid A, Lederer S, Lee H, Lee J, Lee Y, Leppaho E, Lewis R, Li J, Li L, Liley J, Lim W, Lin C, Liu Y, Lopez Y, Low J, Lysenko A, Machado D, Madhukar N, De Maeyer D, Malpartida A, Mamitsuka H, Marabita F, Marchal K, Marttinen P, Mason D, Mazaheri A, Mehmood A, Mehreen A, Michaut M, Miller R, Mitsopoulos C, Modos D, Van Moerbeke M, Moo K, Motsinger-Reif A, Movva R, Muraru S, Muratov E, Mushthofa M, Nagarajan N, Nakken S, Nath A, Neuvial P, Newton R, Ning Z, De Niz C, Oliva B, Olsen C, Palmeri A, Panesar B, Papadopoulos S, Park J, Park S, Pawitan Y, Peluso D, Pendyala S, Peng J, Perfetto L, Pirro S, Plevritis S, Politi R, Poon H, Porta E, Prellner I, Preuer K, Pujana M, Ramnarine R, Reid J, Reyal F, Richardson S, Ricketts C, Rieswijk L, Rocha M, Rodriguez-Gonzalvez C, Roell K, Rotroff D, de Ruiter J, Rukawa P, Sadacca B, Safikhani Z, Safitri F, Sales-Pardo M, Sauer S, Schlichting M, Seoane J, Serra J, Shang M, Sharma A, Sharma H, Shen Y, Shiga M, Shin M, Shkedy Z, Shopsowitz K, Sinai S, Skola D, Smirnov P, Soerensen I, Soerensen P, Song J, Song S, Soufan O, Spitzmueller A, Steipe B, Suphavilai C, Tamayo S, Tamborero D, Tang J, Tanoli Z, Tarres-Deulofeu M, Tegner J, Thommesen L, Tonekaboni S, Tran H, De Troyer E, Truong A, Tsunoda T, Turu G, Tzeng G, Verbeke L, Videla S, Vis D, Voronkov A, Votis K, Wang A, Wang H, Wang P, Wang S, Wang W, Wang X, Wennerberg K, Wernisch L, Wessels L, van Westen G, Westerman B, White S, Willighagen E, Wurdinger T, Xie L, Xie S, Xu H, Yadav B, Yau C, Yeerna H, Yin J, Yu M, Yun S, Zakharov A, Zamichos A, Zanin M, Zeng L, Zenil H, Zhang F, Zhang P, Zhang W, Zhao H, Zhao L, Zheng W, Zoufir A, Zucknick M, AstraZeneca-Sanger Drug Combinatio, Ege Üniversitesi, Gönen, Mehmet (ORCID 0000-0002-2483-075X & YÖK ID 237468), Menden, Michael P., Wang, Dennis, Mason, Mike J., Szalai, Bence, Bulusu, Krishna C., Guan, Yuanfang, Yu, Thomas, Kang, Jaewoo, Jeon, Minji, Wolfinger, Russ, Nguyen, Tin, Zaslavskiy, Mikhail, Jang, In Sock, Ghazoui, Zara, Ahsen, Mehmet Eren, Vogel, Robert, Neto, Elias Chaibub, Norman, Thea, Tang, Eric K. Y., Garnett, Mathew J., Di Veroli, Giovanni Y., Fawell, Stephen, Stolovitzky, Gustavo, Guinney, Justin, Dry, Jonathan R., Saez-Rodriguez, Julio, Abante, Jordi, Abecassis, Barbara Schmitz, Aben, Nanne, Aghamirzaie, Delasa, Aittokallio, Tero, Akhtari, Farida S., Al-lazikani, Bissan, Alam, Tanvir, Allam, Amin, Allen, Chad, de Almeida, Mariana Pelicano, Altarawy, Doaa, Alves, Vinicius, Amadoz, Alicia, Anchang, Benedict, Antolin, Albert A., Ash, Jeremy R., Romeo Aznar, Victoria, Ba-alawi, Wail, Bagheri, Moeen, Bajic, Vladimir, Ball, Gordon, Ballester, Pedro J., Baptista, Delora, Bare, Christopher, Bateson, Mathilde, Bender, Andreas, Bertrand, Denis, Wijayawardena, Bhagya, Boroevich, Keith A., Bosdriesz, Evert, Bougouffa, Salim, Bounova, Gergana, Brouwer, Thomas, Bryant, Barbara, Calaza, Manuel, Calderone, Alberto, Calza, Stefano, Capuzzi, Stephen, Carbonell-Caballero, Jose, Carlin, Daniel, Carter, Hannah, Castagnoli, Luisa, Celebi, Remzi, Cesareni, Gianni, Chang, Hyeokyoon, Chen, Guocai, Chen, Haoran, Chen, Huiyuan, Cheng, Lijun, Chernomoretz, Ariel, Chicco, Davide, Cho, Kwang-Hyun, Cho, Sunghwan, Choi, Daeseon, Choi, Jaejoon, Choi, Kwanghun, Choi, Minsoo, De Cock, Martine, Coker, Elizabeth, Cortes-Ciriano, Isidro, Cserzo, Miklos, Cubuk, Cankut, Curtis, Christina, Van Daele, Dries, Dang, Cuong C., Dijkstra, Tjeerd, Dopazo, Joaquin, Draghici, Sorin, Drosou, Anastasios, Dumontier, Michel, Ehrhart, Friederike, Eid, Fatma-Elzahraa, ElHefnawi, Mahmoud, Elmarakeby, Haitham, van Engelen, Bo, Engin, Hatice Billur, de Esch, Iwan, Evelo, Chris, Falcao, Andre O., Farag, Sherif, Fernandez-Lozano, Carlos, Fisch, Kathleen, Flobak, Asmund, Fornari, Chiara, Foroushani, Amir B. K., Fotso, Donatien Chedom, Fourches, Denis, Friend, Stephen, Frigessi, Arnoldo, Gao, Feng, Gao, Xiaoting, Gerold, Jeffrey M., Gestraud, Pierre, Ghosh, Samik, Gillberg, Jussi, Godoy-Lorite, Antonia, Godynyuk, Lizzy, Godzik, Adam, Goldenberg, Anna, Gomez-Cabrero, David, de Graaf, Chris, Gray, Harry, Grechkin, Maxim, Guimera, Roger, Guney, Emre, Haibe-Kains, Benjamin, Han, Younghyun, Hase, Takeshi, He, Di, He, Liye, Heath, Lenwood S., Hellton, Kristoffer H., Helmer-Citterich, Manuela, Hidalgo, Marta R., Hidru, Daniel, Hill, Steven M., Hochreiter, Sepp, Hong, Seungpyo, Hovig, Eivind, Hsueh, Ya-Chih, Hu, Zhiyuan, Huang, Justin K., Huang, R. Stephanie, Hunyady, Laszlo, Hwang, Jinseub, Hwang, Tae Hyun, Hwang, Woochang, Hwang, Yongdeuk, Isayev, Olexandr, Walk, Oliver Bear Don't, Jack, John, Jahandideh, Samad, Ji, Jiadong, Jo, Yousang, Kamola, Piotr J., Kanev, Georgi K., Karacosta, Loukia, Karimi, Mostafa, Kaski, Samuel, Kazanov, Marat, Khamis, Abdullah M., Khan, Suleiman Ali, Kiani, Narsis A., Kim, Allen, Kim, Jinhan, Kim, Juntae, Kim, Kiseong, Kim, Kyung, Kim, Sunkyu, Kim, Yongsoo, Kim, Yunseong, Kirk, Paul D. W., Kitano, Hiroaki, Klambauer, Gunter, Knowles, David, Ko, Melissa, Kohn-Luque, Alvaro, Kooistra, Albert J., Kuenemann, Melaine A., Kuiper, Martin, Kurz, Christoph, Kwon, Mijin, van Laarhoven, Twan, Laegreid, Astrid, Lederer, Simone, Lee, Heewon, Lee, Jeon, Lee, Yun Woo, Leppaho, Eemeli, Lewis, Richard, Li, Jing, Li, Lang, Liley, James, Lim, Weng Khong, Lin, Chieh, Liu, Yiyi, Lopez, Yosvany, Low, Joshua, Lysenko, Artem, Machado, Daniel, Madhukar, Neel, De Maeyer, Dries, Malpartida, Ana Belen, Mamitsuka, Hiroshi, Marabita, Francesco, Marchal, Kathleen, Marttinen, Pekka, Mason, Daniel, Mazaheri, Alireza, Mehmood, Arfa, Mehreen, Ali, Michaut, Magali, Miller, Ryan A., Mitsopoulos, Costas, Modos, Dezso, Van Moerbeke, Marijke, Moo, Keagan, Motsinger-Reif, Alison, Movva, Rajiv, Muraru, Sebastian, Muratov, Eugene, Mushthofa, Mushthofa, Nagarajan, Niranjan, Nakken, Sigve, Nath, Aritro, Neuvial, Pierre, Newton, Richard, Ning, Zheng, De Niz, Carlos, Oliva, Baldo, Olsen, Catharina, Palmeri, Antonio, Panesar, Bhawan, Papadopoulos, Stavros, Park, Jaesub, Park, Seonyeong, Park, Sungjoon, Pawitan, Yudi, Peluso, Daniele, Pendyala, Sriram, Peng, Jian, Perfetto, Livia, Pirro, Stefano, Plevritis, Sylvia, Politi, Regina, Poon, Hoifung, Porta, Eduard, Prellner, Isak, Preuer, Kristina, Angel Pujana, Miguel, Ramnarine, Ricardo, Reid, John E., Reyal, Fabien, Richardson, Sylvia, Ricketts, Camir, Rieswijk, Linda, Rocha, Miguel, Rodriguez-Gonzalvez, Carmen, Roell, Kyle, Rotroff, Daniel, de Ruiter, Julian R., Rukawa, Ploy, Sadacca, Benjamin, Safikhani, Zhaleh, Safitri, Fita, Sales-Pardo, Marta, Sauer, Sebastian, Schlichting, Moritz, Seoane, Jose A., Serra, Jordi, Shang, Ming-Mei, Sharma, Alok, Sharma, Hari, Shen, Yang, Shiga, Motoki, Shin, Moonshik, Shkedy, Ziv, Shopsowitz, Kevin, Sinai, Sam, Skola, Dylan, Smirnov, Petr, Soerensen, Izel Fourie, Soerensen, Peter, Song, Je-Hoon, Song, Sang Ok, Soufan, Othman, Spitzmueller, Andreas, Steipe, Boris, Suphavilai, Chayaporn, Tamayo, Sergio Pulido, Tamborero, David, Tang, Jing, Tanoli, Zia-ur-Rehman, Tarres-Deulofeu, Marc, Tegner, Jesper, Thommesen, Liv, Tonekaboni, Seyed Ali Madani, Tran, Hong, De Troyer, Ewoud, Truong, Amy, Tsunoda, Tatsuhiko, Turu, Gabor, Tzeng, Guang-Yo, Verbeke, Lieven, Videla, Santiago, Vis, Daniel, Voronkov, Andrey, Votis, Konstantinos, Wang, Ashley, Wang, Hong-Qiang Horace, Wang, Po-Wei, Wang, Sheng, Wang, Wei, Wang, Xiaochen, Wang, Xin, Wennerberg, Krister, Wernisch, Lorenz, Wessels, Lodewyk, van Westen, Gerard J. P., Westerman, Bart A., White, Simon Richard, Willighagen, Egon, Wurdinger, Tom, Xie, Lei, Xie, Shuilian, Xu, Hua, Yadav, Bhagwan, Yau, Christopher, Yeerna, Huwate, Yin, Jia Wei, Yu, Michael, Yu, MinHwan, Yun, So Jeong, Zakharov, Alexey, Zamichos, Alexandros, Zanin, Massimiliano, Zeng, Li, Zenil, Hector, Zhang, Frederick, Zhang, Pengyue, Zhang, Wei, Zhao, Hongyu, Zhao, Lan, Zheng, Wenjin, Zoufir, Azedine, Zucknick, Manuela, College of Engineering, Department of Industrial Engineering, Institute of Data Science, RS: FSE DACS IDS, Bioinformatica, RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health, RS: FHML MaCSBio, Promovendi NTM, Tero Aittokallio / Principal Investigator, Bioinformatics, Institute for Molecular Medicine Finland, Hu, Z, Fotso, DC, Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden, Michael P. [0000-0003-0267-5792], Mason, Mike J. [0000-0002-5652-7739], Yu, Thomas [0000-0002-5841-0198], Kang, Jaewoo [0000-0001-6798-9106], Nguyen, Tin [0000-0001-8001-9470], Ahsen, Mehmet Eren [0000-0002-4907-0427], Stolovitzky, Gustavo [0000-0002-9618-2819], Guinney, Justin [0000-0003-1477-1888], Saez-Rodriguez, Julio [0000-0002-8552-8976], Apollo - University of Cambridge Repository, Menden, Michael P [0000-0003-0267-5792], Mason, Mike J [0000-0002-5652-7739], Pathology, CCA - Cancer biology and immunology, Medical oncology laboratory, Neurosurgery, Chemistry and Pharmaceutical Sciences, AIMMS, Medicinal chemistry, Universidade do Minho, Department of Computer Science, Professorship Marttinen P., Aalto-yliopisto, and Aalto University
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Drug Resistance ,02 engineering and technology ,13 ,PATHWAY ,Antineoplastic Combined Chemotherapy Protocols ,Molecular Targeted Therapy ,Càncer ,lcsh:Science ,media_common ,Cancer ,Tumor ,Settore BIO/18 ,Settore BIO/11 ,Drug combinations ,High-throughput screening ,Drug Synergism ,purl.org/becyt/ford/1.2 [https] ,Genomics ,Machine Learning ,predictions ,3. Good health ,ddc ,Technologie de l'environnement, contrôle de la pollution ,Benchmarking ,5.1 Pharmaceuticals ,Cancer treatment ,Farmacogenètica ,Science & Technology - Other Topics ,Development of treatments and therapeutic interventions ,0210 nano-technology ,Human ,Drug ,media_common.quotation_subject ,Science ,49/23 ,ADAM17 Protein ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,SDG 3 - Good Health and Well-being ,RESOURCE ,Machine learning ,Genetics ,Chimie ,Humans ,BREAST-CANCER ,CELL ,49/98 ,Science & Technology ,Antineoplastic Combined Chemotherapy Protocol ,45 ,MUTATIONS ,Computational Biology ,Androgen receptor ,Breast-cancer ,Gene ,Cell ,Inhibition ,Resistance ,Pathway ,Mutations ,Landscape ,Resource ,631/114/1305 ,medicine.disease ,Drug synergy ,49 ,030104 developmental biology ,Pharmacogenetics ,Mutation ,Ciências Médicas::Biotecnologia Médica ,lcsh:Q ,631/154/1435/2163 ,Biomarkers ,RESISTANCE ,0301 basic medicine ,ING-INF/06 - BIOINGEGNERIA ELETTRONICA E INFORMATICA ,Statistical methods ,Computer science ,General Physics and Astronomy ,Datasets as Topic ,Drug resistance ,purl.org/becyt/ford/1 [https] ,Phosphatidylinositol 3-Kinases ,Biotecnologia Médica [Ciências Médicas] ,Neoplasms ,Science and technology ,Phosphoinositide-3 Kinase Inhibitors ,Multidisciplinary ,Biomarkers, Tumor ,Cell Line, Tumor ,Drug Antagonism ,Drug Resistance, Neoplasm ,Treatment Outcome ,Pharmacogenetic ,article ,ANDROGEN RECEPTOR ,49/39 ,631/114/2415 ,021001 nanoscience & nanotechnology ,692/4028/67 ,Multidisciplinary Sciences ,317 Pharmacy ,Patient Safety ,Systems biology ,3122 Cancers ,INHIBITION ,Computational biology ,Cell Line ,medicine ,LANDSCAPE ,Physique ,Human Genome ,Data Science ,General Chemistry ,AstraZeneca-Sanger Drug Combination DREAM Consortium ,Astronomie ,GENE ,Good Health and Well Being ,Pharmacogenomics ,Genomic ,Neoplasm ,631/553 ,Phosphatidylinositol 3-Kinase - Abstract
PubMed: 31209238, The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. © 2019, The Author(s)., National Institute for Health Research, NIHR Wellcome Trust, WT: 102696, 206194 Magyar Tudományos Akadémia, MTA Bayer 668858 PrECISE AstraZeneca, We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194)., Competing interests: K.C.B., Z.G., G.Y.D., E.K.Y.T., S.F., and J.R.D. are AstraZeneca employees. K.C.B., Z.G., E.K.Y.T., S.F., and J.R.D. are AstraZeneca shareholders. Y.G. receives personal compensation from Eli Lilly and Company, is a shareholder of Cleerly, Inc., and Ann Arbor Algorithms, Inc. M.G. receives research funding from AstraZeneca and has performed consultancy for Sanofi. The remaining authors declare no competing interests.
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- 2019
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5. Reuse of design pattern measurements for health data
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Queralt-Rosinach, N, Wilkinson, M, Kaliyaperumal, R, Bernabé, CH, Long, Q, Dumontier, M, Schofield, PN, and Roos, M
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Research using health data is challenged by its heterogeneous nature, description and storage. The COVID-19 outbreak made clear that rapid analysis of observations such as clinical measurements across a large number of healthcare providers can have enormous health benefits. This has brought into focus the need for a common model of quantitative health data that enables data exchange and federated computational analysis. The application of ontologies, Semantic Web technologies and the FAIR principles is an approach used by different life science research projects, such as the European Joint Programme on Rare Diseases, to make data and metadata machine readable and thereby reduce the barriers for data sharing and analytics and harness health data for discovery. Here, we show the reuse of a pattern for measurements to model diverse health data, to demonstrate and raise visibility of the usefulness of this pattern for biomedical research.
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- 2021
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6. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
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Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Hu, Z, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., Bosdriesz E., Bougouffa S., Bounova G., Brouwer T., Bryant B., Calaza M., Calderone A., Calza S., Capuzzi S., Carbonell-Caballero J., Carlin D., Carter H., Castagnoli L., Celebi R., Cesareni G., Chang H., Chen G., Chen H., Cheng L., Chernomoretz A., Chicco D., Cho K. -H., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzo M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F. -E., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., Gestraud P., Ghosh S., Gillberg J., Godoy-Lorite A., Godynyuk L., Godzik A., Goldenberg A., Gomez-Cabrero D., Gonen M., de Graaf C., Gray H., Grechkin M., Guimera R., Guney E., Haibe-Kains B., Han Y., Hase T., He D., He L., Heath L. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y. -C., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don't Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim K., Kim S., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., Moo K., Motsinger-Reif A., Movva R., Muraru S., Muratov E., Mushthofa M., Nagarajan N., Nakken S., Nath A., Neuvial P., Newton R., Ning Z., Niz C. D., Oliva B., Olsen C., Palmeri A., Panesar B., Papadopoulos S., Park J., Park S., Pawitan Y., Peluso D., Pendyala S., Peng J., Perfetto L., Pirro S., Plevritis S., Politi R., Poon H., Porta E., Prellner I., Preuer K., Pujana M. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M. -M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J. -H., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z. -U. -R., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G. -Y., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. -Q. H., Wang P. -W., Wang S., Wang W., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M. H., Yun S. J., Zakharov A., Zamichos A., Zanin M., Zeng L., Zenil H., Zhang F., Zhang P., Zhang W., Zhao H., Zhao L., Zheng W., Zoufir A., Zucknick M., Jang I. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., Saez-Rodriguez J., Menden, M, Wang, D, Mason, M, Szalai, B, Bulusu, K, Guan, Y, Yu, T, Kang, J, Jeon, M, Wolfinger, R, Nguyen, T, Zaslavskiy, M, Abante, J, Abecassis, B, Aben, N, Aghamirzaie, D, Aittokallio, T, Akhtari, F, Al-lazikani, B, Alam, T, Allam, A, Allen, C, de Almeida, M, Altarawy, D, Alves, V, Amadoz, A, Anchang, B, Antolin, A, Ash, J, Aznar, V, Ba-alawi, W, Bagheri, M, Bajic, V, Ball, G, Ballester, P, Baptista, D, Bare, C, Bateson, M, Bender, A, Bertrand, D, Wijayawardena, B, Boroevich, K, Bosdriesz, E, Bougouffa, S, Bounova, G, Brouwer, T, Bryant, B, Calaza, M, Calderone, A, Calza, S, Capuzzi, S, Carbonell-Caballero, J, Carlin, D, Carter, H, Castagnoli, L, Celebi, R, Cesareni, G, Chang, H, Chen, G, Chen, H, Cheng, L, Chernomoretz, A, Chicco, D, Cho, K, Cho, S, Choi, D, Choi, J, Choi, K, Choi, M, Cock, M, Coker, E, Cortes-Ciriano, I, Cserzo, M, Cubuk, C, Curtis, C, Daele, D, Dang, C, Dijkstra, T, Dopazo, J, Draghici, S, Drosou, A, Dumontier, M, Ehrhart, F, Eid, F, Elhefnawi, M, Elmarakeby, H, van Engelen, B, Engin, H, de Esch, I, Evelo, C, Falcao, A, Farag, S, Fernandez-Lozano, C, Fisch, K, Flobak, A, Fornari, C, Foroushani, A, Fotso, D, Fourches, D, Friend, S, Frigessi, A, Gao, F, Gao, X, Gerold, J, Gestraud, P, Ghosh, S, Gillberg, J, Godoy-Lorite, A, Godynyuk, L, Godzik, A, Goldenberg, A, Gomez-Cabrero, D, Gonen, M, de Graaf, C, Gray, H, Grechkin, M, Guimera, R, Guney, E, Haibe-Kains, B, Han, Y, Hase, T, He, D, He, L, Heath, L, Hellton, K, Helmer-Citterich, M, Hidalgo, M, Hidru, D, Hill, S, Hochreiter, S, Hong, S, Hovig, E, Hsueh, Y, Hu, Z, Huang, J, Huang, R, Hunyady, L, Hwang, J, Hwang, T, Hwang, W, Hwang, Y, Isayev, O, Don't Walk, O, Jack, J, Jahandideh, S, Ji, J, Jo, Y, Kamola, P, Kanev, G, Karacosta, L, Karimi, M, Kaski, S, Kazanov, M, Khamis, A, Khan, S, Kiani, N, Kim, A, Kim, J, Kim, K, Kim, S, Kim, Y, Kirk, P, Kitano, H, Klambauer, G, Knowles, D, Ko, M, Kohn-Luque, A, Kooistra, A, Kuenemann, M, Kuiper, M, Kurz, C, Kwon, M, van Laarhoven, T, Laegreid, A, Lederer, S, Lee, H, Lee, J, Lee, Y, Lepp_aho, E, Lewis, R, Li, J, Li, L, Liley, J, Lim, W, Lin, C, Liu, Y, Lopez, Y, Low, J, Lysenko, A, Machado, D, Madhukar, N, Maeyer, D, Malpartida, A, Mamitsuka, H, Marabita, F, Marchal, K, Marttinen, P, Mason, D, Mazaheri, A, Mehmood, A, Mehreen, A, Michaut, M, Miller, R, Mitsopoulos, C, Modos, D, Moerbeke, M, Moo, K, Motsinger-Reif, A, Movva, R, Muraru, S, Muratov, E, Mushthofa, M, Nagarajan, N, Nakken, S, Nath, A, Neuvial, P, Newton, R, Ning, Z, Niz, C, Oliva, B, Olsen, C, Palmeri, A, Panesar, B, Papadopoulos, S, Park, J, Park, S, Pawitan, Y, Peluso, D, Pendyala, S, Peng, J, Perfetto, L, Pirro, S, Plevritis, S, Politi, R, Poon, H, Porta, E, Prellner, I, Preuer, K, Pujana, M, Ramnarine, R, Reid, J, Reyal, F, Richardson, S, Ricketts, C, Rieswijk, L, Rocha, M, Rodriguez-Gonzalvez, C, Roell, K, Rotroff, D, de Ruiter, J, Rukawa, P, Sadacca, B, Safikhani, Z, Safitri, F, Sales-Pardo, M, Sauer, S, Schlichting, M, Seoane, J, Serra, J, Shang, M, Sharma, A, Sharma, H, Shen, Y, Shiga, M, Shin, M, Shkedy, Z, Shopsowitz, K, Sinai, S, Skola, D, Smirnov, P, Soerensen, I, Soerensen, P, Song, J, Song, S, Soufan, O, Spitzmueller, A, Steipe, B, Suphavilai, C, Tamayo, S, Tamborero, D, Tang, J, Tanoli, Z, Tarres-Deulofeu, M, Tegner, J, Thommesen, L, Tonekaboni, S, Tran, H, Troyer, E, Truong, A, Tsunoda, T, Turu, G, Tzeng, G, Verbeke, L, Videla, S, Vis, D, Voronkov, A, Votis, K, Wang, A, Wang, H, Wang, P, Wang, S, Wang, W, Wang, X, Wennerberg, K, Wernisch, L, Wessels, L, van Westen, G, Westerman, B, White, S, Willighagen, E, Wurdinger, T, Xie, L, Xie, S, Xu, H, Yadav, B, Yau, C, Yeerna, H, Yin, J, Yu, M, Yun, S, Zakharov, A, Zamichos, A, Zanin, M, Zeng, L, Zenil, H, Zhang, F, Zhang, P, Zhang, W, Zhao, H, Zhao, L, Zheng, W, Zoufir, A, Zucknick, M, Jang, I, Ghazoui, Z, Ahsen, M, Vogel, R, Neto, E, Norman, T, Tang, E, Garnett, M, Veroli, G, Fawell, S, Stolovitzky, G, Guinney, J, Dry, J, Saez-Rodriguez, J, Menden M. P., Wang D., Mason M. J., Szalai B., Bulusu K. C., Guan Y., Yu T., Kang J., Jeon M., Wolfinger R., Nguyen T., Zaslavskiy M., Abante J., Abecassis B. S., Aben N., Aghamirzaie D., Aittokallio T., Akhtari F. S., Al-lazikani B., Alam T., Allam A., Allen C., de Almeida M. P., Altarawy D., Alves V., Amadoz A., Anchang B., Antolin A. A., Ash J. R., Aznar V. R., Ba-alawi W., Bagheri M., Bajic V., Ball G., Ballester P. J., Baptista D., Bare C., Bateson M., Bender A., Bertrand D., Wijayawardena B., Boroevich K. A., Bosdriesz E., Bougouffa S., Bounova G., Brouwer T., Bryant B., Calaza M., Calderone A., Calza S., Capuzzi S., Carbonell-Caballero J., Carlin D., Carter H., Castagnoli L., Celebi R., Cesareni G., Chang H., Chen G., Chen H., Cheng L., Chernomoretz A., Chicco D., Cho K. -H., Cho S., Choi D., Choi J., Choi K., Choi M., Cock M. D., Coker E., Cortes-Ciriano I., Cserzo M., Cubuk C., Curtis C., Daele D. V., Dang C. C., Dijkstra T., Dopazo J., Draghici S., Drosou A., Dumontier M., Ehrhart F., Eid F. -E., ElHefnawi M., Elmarakeby H., van Engelen B., Engin H. B., de Esch I., Evelo C., Falcao A. O., Farag S., Fernandez-Lozano C., Fisch K., Flobak A., Fornari C., Foroushani A. B. K., Fotso D. C., Fourches D., Friend S., Frigessi A., Gao F., Gao X., Gerold J. M., Gestraud P., Ghosh S., Gillberg J., Godoy-Lorite A., Godynyuk L., Godzik A., Goldenberg A., Gomez-Cabrero D., Gonen M., de Graaf C., Gray H., Grechkin M., Guimera R., Guney E., Haibe-Kains B., Han Y., Hase T., He D., He L., Heath L. S., Hellton K. H., Helmer-Citterich M., Hidalgo M. R., Hidru D., Hill S. M., Hochreiter S., Hong S., Hovig E., Hsueh Y. -C., Hu Z., Huang J. K., Huang R. S., Hunyady L., Hwang J., Hwang T. H., Hwang W., Hwang Y., Isayev O., Don't Walk O. B., Jack J., Jahandideh S., Ji J., Jo Y., Kamola P. J., Kanev G. K., Karacosta L., Karimi M., Kaski S., Kazanov M., Khamis A. M., Khan S. A., Kiani N. A., Kim A., Kim J., Kim K., Kim S., Kim Y., Kirk P. D. W., Kitano H., Klambauer G., Knowles D., Ko M., Kohn-Luque A., Kooistra A. J., Kuenemann M. A., Kuiper M., Kurz C., Kwon M., van Laarhoven T., Laegreid A., Lederer S., Lee H., Lee J., Lee Y. W., Lepp_aho E., Lewis R., Li J., Li L., Liley J., Lim W. K., Lin C., Liu Y., Lopez Y., Low J., Lysenko A., Machado D., Madhukar N., Maeyer D. D., Malpartida A. B., Mamitsuka H., Marabita F., Marchal K., Marttinen P., Mason D., Mazaheri A., Mehmood A., Mehreen A., Michaut M., Miller R. A., Mitsopoulos C., Modos D., Moerbeke M. V., Moo K., Motsinger-Reif A., Movva R., Muraru S., Muratov E., Mushthofa M., Nagarajan N., Nakken S., Nath A., Neuvial P., Newton R., Ning Z., Niz C. D., Oliva B., Olsen C., Palmeri A., Panesar B., Papadopoulos S., Park J., Park S., Pawitan Y., Peluso D., Pendyala S., Peng J., Perfetto L., Pirro S., Plevritis S., Politi R., Poon H., Porta E., Prellner I., Preuer K., Pujana M. A., Ramnarine R., Reid J. E., Reyal F., Richardson S., Ricketts C., Rieswijk L., Rocha M., Rodriguez-Gonzalvez C., Roell K., Rotroff D., de Ruiter J. R., Rukawa P., Sadacca B., Safikhani Z., Safitri F., Sales-Pardo M., Sauer S., Schlichting M., Seoane J. A., Serra J., Shang M. -M., Sharma A., Sharma H., Shen Y., Shiga M., Shin M., Shkedy Z., Shopsowitz K., Sinai S., Skola D., Smirnov P., Soerensen I. F., Soerensen P., Song J. -H., Song S. O., Soufan O., Spitzmueller A., Steipe B., Suphavilai C., Tamayo S. P., Tamborero D., Tang J., Tanoli Z. -U. -R., Tarres-Deulofeu M., Tegner J., Thommesen L., Tonekaboni S. A. M., Tran H., Troyer E. D., Truong A., Tsunoda T., Turu G., Tzeng G. -Y., Verbeke L., Videla S., Vis D., Voronkov A., Votis K., Wang A., Wang H. -Q. H., Wang P. -W., Wang S., Wang W., Wang X., Wennerberg K., Wernisch L., Wessels L., van Westen G. J. P., Westerman B. A., White S. R., Willighagen E., Wurdinger T., Xie L., Xie S., Xu H., Yadav B., Yau C., Yeerna H., Yin J. W., Yu M., Yu M. H., Yun S. J., Zakharov A., Zamichos A., Zanin M., Zeng L., Zenil H., Zhang F., Zhang P., Zhang W., Zhao H., Zhao L., Zheng W., Zoufir A., Zucknick M., Jang I. S., Ghazoui Z., Ahsen M. E., Vogel R., Neto E. C., Norman T., Tang E. K. Y., Garnett M. J., Veroli G. Y. D., Fawell S., Stolovitzky G., Guinney J., Dry J. R., and Saez-Rodriguez J.
- Abstract
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
- Published
- 2019
7. Experience: Automated Prediction of Experimental Metadata from Scientific Publications
- Author
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Nayak, S., Zaveri, A., Serrano, P.H., Dumontier, M., Nayak, S., Zaveri, A., Serrano, P.H., and Dumontier, M.
- Abstract
While there exists an abundance of open biomedical data, the lack of high-quality metadata makes it challenging for others to find relevant datasets and to reuse them for another purpose. In particular, metadata are useful to understand the nature and provenance of the data. A common approach to improving the quality of metadata relies on expensive human curation, which itself is time-consuming and also prone to error. Towards improving the quality of metadata, we use scientific publications to automatically predict metadata key:value pairs. For prediction, we use a Convolutional Neural Network (CNN) and a Bidirectional Long-short term memory network (BiLSTM). We focus our attention on the NCBI Disease Corpus, which is used for training the CNN and BiLSTM. We perform two different kinds of experiments with these two architectures: (1) we predict the disease names by using their unique ID in the MeSH ontology and (2) we use the tree structures of MeSH ontology to move up in the hierarchy of these disease terms, which reduces the number of labels. We also perform various multi-label classification techniques for the above-mentioned experiments. We find that in both cases CNN achieves the best results in predicting the superclasses for disease with an accuracy of 83%.
- Published
- 2021
8. Sleeping Beauties in Case Law
- Author
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Hernandez Serrano, P.V., Moodley, K., Van Dijck, G., Dumontier, M., Villata, S, Harasta, J, Kremen, P, RS: FSE Studio Europa Maastricht, RS: FdR Research Group Law and Tech Lab, Institute of Data Science, RS: FSE DACS IDS, RS: FdR not Institute related, Private Law, RS: FdR Institute M-EPLI, RS: FdR IC Aansprakelijkheid, and RS: FSE BISS
- Subjects
Citation Networks ,History ,case law ,network science ,Law ,Common law ,court decisions ,empirical legal research ,citation analysis ,Sleeping Beauties in Science ,scientometrics ,network analysis ,Computational Legal Research - Abstract
A challenge in computational legal research is the quantitative assessment of “relevance” in a network of court decisions. The term “sleeping beauty” (SB) was coined to denote an article that received almost no attention immediately after publication, but suddenly received multiple citations many years later. These publications can be identified by calculating their Beauty coefficient (B-coefficient). In this contribution, we apply approaches used for identifying SBs to decisions arising from the Court of Justice of the European Union (CJEU). We compared B-coefficients of CJEU cases with their centrality scores from classical algorithms from network analysis, finding that these measures tend to correlate. We discuss the implications of this that are interesting for legal scholars, acknowledging that future work is required to calibrate the scale of the time variable in the B-coefficient formula for finer-grained application to case law. Our study’s setup provides a foundation for new case law analytics methodologies that extends the power of traditional network analysis techniques for answering questions about the behavior of European courts.
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- 2020
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9. Data validation and schema interoperability
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Núria Queralt-Rosinach, Garcia Castro Lj, Thomas Liener, Jerven Bolleman, Labra-Gayo Je, dumontier m, Simon Jupp, Chunlei Wu, and Tazro Ohta
- Subjects
Schema (genetic algorithms) ,Information retrieval ,Computer science ,Interoperability ,Data validation - Abstract
Validating RDF data becomes necessary in order to ensure data compliance against the conceptualization model it follows, e.g., schema or ontology behind the data, and improve data consistency and completeness. There are different approaches to validate RDF data, for instance, JSON schema, particularly for data in JSONLD format, as well as Shape Expression and Shapes Constraint Language, which can be used with other serialization as well, e.g., RDF/XML or Turtle. Currently, no validation approach is prevalent regarding others, selection commonly depends on data characteristics, background knowledge and personal preferences . In some cases, the approaches are interchangeable; however, that is not always the case, making it necessary to identify a subset among them that can be seamlessly translated from one to another. During the NBDC/DBCLS 2019 BioHackathon, we worked on a variety of topics related to RDF data validation, including (i) development of ShEx shapes for a number of datasets, (ii) development of a tool to semi-automatically create ShEx shapes, (iii) improvements to the RDFShape tool, and (iv) enabling validation schema conversion from one format to the other. Here we report on our BioHackathon achievements.
- Published
- 2020
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10. ResearchFlow: Understanding the Knowledge Flow Between Academia and Industry
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Keet, CM, Dumontier, M, Salatino, A, Osborne, F, Motta, E, Salatino A, Osborne F, Motta E, Keet, CM, Dumontier, M, Salatino, A, Osborne, F, Motta, E, Salatino A, Osborne F, and Motta E
- Abstract
Understanding, monitoring, and predicting the flow of knowledge between academia and industry is of critical importance for a variety of stakeholders, including governments, funding bodies, researchers, investors, and companies. To this purpose, we introduce ResearchFlow, an approach that integrates semantic technologies and machine learning to quantifying the diachronic behaviour of research topics across academia and industry. ResearchFlow exploits the novel Academia/Industry DynAmics (AIDA) Knowledge Graph in order to characterize each topic according to the frequency in time of the related i) publications from academia, ii) publications from industry, iii) patents from academia, and iv) patents from industry. This representation is then used to produce several analytics regarding the academia/industry knowledge flow and to forecast the impact of research topics on industry. We applied ResearchFlow to a dataset of 3.5M papers and 2M patents in Computer Science and highlighted several interesting patterns. We found that 89.8% of the topics first emerge in academic publications, which typically precede industrial publications by about 5.6 years and industrial patents by about 6.6 years. However this does not mean that academia always dictates the research agenda. In fact, our analysis also shows that industrial trends tend to influence academia more than academic trends affect industry. We evaluated ResearchFlow on the task of forecasting the impact of research topics on the industrial sector and found that its granular characterization of topics improves significantly the performance with respect to alternative solutions.
- Published
- 2020
11. The Biomolecular Interaction Network Database and related tools 2005 update
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Alfarano, C., Andrade, C. E., Anthony, K., Bahroos, N., Bajec, M., Bantoft, K., Betel, D., Bobechko, B., Boutilier, K., Burgess, E., Buzadzija, K., Cavero, R., D'Abreo, C., Donaldson, I., Dorairajoo, D., Dumontier, M. J., Dumontier, M. R., Earles, V., Farrall, R., Feldman, H., Garderman, E., Gong, Y., Gonzaga, R., Grytsan, V., Gryz, E., Gu, V., Haldorsen, E., Halupa, A., Haw, R., Hrvojic, A., Hurrell, L., Isserlin, R., Jack, F., Juma, F., Khan, A., Kon, T., Konopinsky, S., Le, V., Lee, E., Ling, S., Magidin, M., Moniakis, J., Montojo, J., Moore, S., Muskat, B., Ng, I., Paraiso, J. P., Parker, B., Pintilie, G., Pirone, R., Salama, J. J., Sgro, S., Shan, T., Shu, Y., Siew, J., Skinner, D., Snyder, K., Stasiuk, R., Strumpf, D., Tuekam, B., Tao, S., Wang, Z., White, M., Willis, R., Wolting, C., Wong, S., Wrong, A., Xin, C., Yao, R., Yates, B., Zhang, S., Zheng, K., Pawson, T., Ouellette, B. F. F., and Hogue, C. W. V.
- Published
- 2005
12. Putting the GDPR into Practice: Difficulties and Uncertainties Experienced in the Conduct of Big Data Health Research
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Wouters, B., primary, Shaw, D., additional, Sun, C., additional, Ippel, L., additional, van Soest, J., additional, van den Berg, B., additional, Mussmann, O., additional, Koster, A., additional, van der Kallen, C., additional, van Oppen, C., additional, Dekker, A., additional, Dumontier, M., additional, and Townend, D., additional
- Published
- 2021
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13. Circulating Insulin-Like Growth Factor System Changes in Women with Acute Estrogen Deficiency Induced by GnRH Agonist
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Poiraudeau, S., Roux, C., De Ceuninck, F., Tsagris, L., Borderie, D., Cherruau, B., Dumontier, M.-F., and Corvol, M.
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- 1997
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14. Global investigation of protein–protein interactions in yeast Saccharomyces cerevisiae using re-occurring short polypeptide sequences
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Pitre, S., North, C., Alamgir, M., Jessulat, M., Chan, A., Luo, X., Green, J. R., Dumontier, M., Dehne, F., and Golshani, A.
- Published
- 2008
15. The FAIR Guiding Principles for scientific data management and stewardship (vol 15, 160018, 2016)
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Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.W., Santos, L.B.D., Bourne, P.E., Bouwman, J., Brookes, A.J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C.T., Finkers, R., Gonzalez-Beltran, A., Gray, A.J.G., Groth, P., Goble, C., Grethe, J.S., Heringa, J., Hoen, P.A.C.'., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S.J., Martone, M.E., Mons, A., Packer, A.L., Persson, B., Rocca-Serra, P., Roos, M., Schaik, R. van, Sansone, S.A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M.A., Thompson, M., Lei, J. van der, Mulligen, E. van, Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.
- Published
- 2019
16. Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent
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van Kesteren, E.-J., Sun, C., Oberski, D.L., Dumontier, M., Ippel, L., van Kesteren, E.-J., Sun, C., Oberski, D.L., Dumontier, M., and Ippel, L.
- Abstract
Combining data from varied sources has considerable potential for knowledge discovery: collaborating data parties can mine data in an expanded feature space, allowing them to explore a larger range of scientific questions. However, data sharing among different parties is highly restricted by legal conditions, ethical concerns, and / or data volume. Fueled by these concerns, the fields of cryptography and distributed learning have made great progress towards privacy-preserving and distributed data mining. However, practical implementations have been hampered by the limited scope or computational complexity of these methods. In this paper, we greatly extend the range of analyses available for vertically partitioned data, i.e., data collected by separate parties with different features on the same subjects. To this end, we present a novel approach for privacy-preserving generalized linear models, a fundamental and powerful framework underlying many prediction and classification procedures. We base our method on a distributed block coordinate descent algorithm to obtain parameter estimates, and we develop an extension to compute accurate standard errors without additional communication cost. We critically evaluate the information transfer for semi-honest collaborators and show that our protocol is secure against data reconstruction. Through both simulated and real-world examples we illustrate the functionality of our proposed algorithm. Without leaking information, our method performs as well on vertically partitioned data as existing methods on combined data -- all within mere minutes of computation time. We conclude that our method is a viable approach for vertically partitioned data analysis with a wide range of real-world applications.
- Published
- 2019
17. Reporting Standards and Critical Appraisal of Prediction Models
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Kubben, P., Dumontier, M., Dekker, A., Wee, L., Kuijk, S.M.J. Van, Dankers, F.J.W.M., Traverso, A., Welch, M., Kubben, P., Dumontier, M., Dekker, A., Wee, L., Kuijk, S.M.J. Van, Dankers, F.J.W.M., Traverso, A., and Welch, M.
- Abstract
Item does not contain fulltext, Prediction models have the potential to positively influence clinical decision-making and thus the overall quality of healthcare. The translational gap needs to be bridged between development of complex statistical models requiring multiple predictors and widespread usage in clinical consultation. A recent review found that inadequate quality of reporting of prediction modelling studies could be a contributing factor in slow transition to the clinic. This chapter emphasises the importance of high-quality reporting of modelling studies and the need for critical appraisal to understand the potential issues limiting generalizability of published models. Evidence synthesis (such as systematic reviews and pooled analysis of disparate models) are relatively under-represented in literature, though methodological studies and guidelines are now starting to appear.
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- 2019
18. Preparing Data for Predictive Modelling
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Kubben, P., Dumontier, M., Dekker, A., Kuijk, S.M.J. Van, Dankers, F.J.W.M., Traverso, A., Wee, L., Kubben, P., Dumontier, M., Dekker, A., Kuijk, S.M.J. Van, Dankers, F.J.W.M., Traverso, A., and Wee, L.
- Abstract
Item does not contain fulltext, This is the first chapter of five that cover an introduction to developing and validating models for predicting outcomes for the individual patient. Such prediction models can be used for predicting the occurrence or recurrence of an event, or of the most likely value on a continuous outcome. We will mainly focus on the prediction of binary outcomes, such as the occurrence of a complication, recurrence of disease, the presence of metastases, remission, survival, etc. This chapter deals with the selection of an appropriate study design for a study on prediction, and on methods to manipulate the data before the statistical modelling can begin.
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- 2019
19. Data at Scale
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Kubben, P., Dumontier, M., Dekker, A., Traverso, A., Dankers, F.J.W.M., Wee, L., Kuijk, S.M.J. Van, Kubben, P., Dumontier, M., Dekker, A., Traverso, A., Dankers, F.J.W.M., Wee, L., and Kuijk, S.M.J. Van
- Abstract
Item does not contain fulltext, Pre-requisites to better understand the chapter: basic knowledge of major sources of clinical data. Logical position of the chapter with respect to the previous chapter: in the previous chapter, you have learned what the major sources of clinical data are. In this chapter, we will dive into the main characteristics of presented data sources. In particular, we will learn how to distinguish and classify data according to its scale. Learning objectives: you will learn the major differences between data sources presented in previous chapters; how clinical data can be classified according to its scale. You will get familiar with the concept of 'big' clinical data; you will learn which are the major concerns limiting 'big' data exchange.
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- 2019
20. Prediction Modeling Methodology
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Kubben, P., Dumontier, M., Dekker, A., Dankers, F.J.W.M., Traverso, A., Wee, L., Kuijk, S.M.J. Van, Kubben, P., Dumontier, M., Dekker, A., Dankers, F.J.W.M., Traverso, A., Wee, L., and Kuijk, S.M.J. Van
- Abstract
Item does not contain fulltext, In the previous chapter, you have learned how to prepare your data before you start the process of generating a predictive model. In this chapter, you will learn how to make a predictive model using very common regression techniques and how to evaluate the performance of a model. In the next chapter we will then look at more advanced machine learning techniques that have become increasingly popular in recent years.
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- 2019
21. Diving Deeper into Models
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Kubben, P., Dumontier, M., Dekker, A., Traverso, A., Dankers, F., Osong, B., Wee, L., Kuijk, S.M.J. Van, Kubben, P., Dumontier, M., Dekker, A., Traverso, A., Dankers, F., Osong, B., Wee, L., and Kuijk, S.M.J. Van
- Abstract
Item does not contain fulltext, Pre-requisites to better understand the chapter: knowledge of the major steps and procedures of developing a clinical prediction model. Logical position of the chapter with respect to the previous chapter: in the last chapters, you have learned how to develop and validate a clinical prediction model. You have been learning logistic regression as main algorithm to build the model. However, several different more complex algorithms can be used to build a clinical prediction model. In this chapter, the main machine learning based algorithms will be presented to you. Learning objectives: you will be presented with the definitions of: machine learning, supervised and unsupervised learning. The major algorithms for the last two categories will be introduced.
- Published
- 2019
22. Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent
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Leerstoel Klugkist, Methodology and statistics for the behavioural and social sciences, van Kesteren, E.-J., Sun, C., Oberski, D.L., Dumontier, M., Ippel, L., Leerstoel Klugkist, Methodology and statistics for the behavioural and social sciences, van Kesteren, E.-J., Sun, C., Oberski, D.L., Dumontier, M., and Ippel, L.
- Published
- 2019
23. Dual effects of 17β-oestradiol on interleukin 1β-induced proteoglycan degradation in chondrocytes
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Richette, P, Dumontier, M F, François, M, Tsagris, L, Korwin-Zmijowska, C, Rannou, F, and Corvol, M T
- Published
- 2004
24. The EU’s General Data Protection Regulation (GDPR) in a Research Context
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Mondschein, Christopher, Monda, Cosimo, Knubben, P., Dumontier, M., Dekker, A., Private Law, Maastr Inst for Transnat Legal Research, RS: FdR, and RS: FdR not Institute related
- Subjects
0301 basic medicine ,Personal data ,Standardization ,Research context ,Encryption ,Fundamental rights ,0603 philosophy, ethics and religion ,Data Protection Directive ,03 medical and health sciences ,Clinical Trials Regulation ,Data Protection Act 1998 ,Special categories of personal data ,Pseudonymisation ,Data protection ,Law and economics ,European Union law ,Anonymisation ,06 humanities and the arts ,Research exemption ,030104 developmental biology ,Privacy ,General Data Protection Regulation ,060301 applied ethics ,Business ,General Data Protection Regulation (GDPR) - Abstract
This chapter introduces the rational and regulatory mechanism underlying the EU data protection framework with specific focus on the EU’s General Data Protection Regulation (GDPR). It outlines the applicability of the research exemption included in the GDPR and discusses further or secondary use of personal data for research purposes.
- Published
- 2018
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25. The center for expanded data annotation and retrieval
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Musen, MA, Bean, CA, Cheung, K-H, Dumontier, M, Durante, KA, Gevaert, O, Gonzalez-Beltran, AN, Khatri, P, Kleinstein, SH, O'Connor, MJ, Pouliot, Y, Rocca-Serra, P, Sansone, S-A, Wiser, JA, and The CEDAR Team
- Subjects
data curation ,Biomedical Research ,data collection ,Information retrieval ,Data element ,Data curation ,Computer science ,Information Storage and Retrieval ,Data discovery ,Meta Data Services ,datasets as topic ,Health Informatics ,Information repository ,United States ,Metadata repository ,Metadata ,standards ,Data Mining ,Humans ,Brief Communications on Big Data ,biological ontologies ,Datasets as Topic - Abstract
The Center for Expanded Data Annotation and Retrieval is studying the creation of comprehensive and expressive metadata for biomedical datasets to facilitate data discovery, data interpretation, and data reuse. We take advantage of emerging community-based standard templates for describing different kinds of biomedical datasets, and we investigate the use of computational techniques to help investigators to assemble templates and to fill in their values. We are creating a repository of metadata from which we plan to identify metadata patterns that will drive predictive data entry when filling in metadata templates. The metadata repository not only will capture annotations specified when experimental datasets are initially created, but also will incorporate links to the published literature, including secondary analyses and possible refinements or retractions of experimental interpretations. By working initially with the Human Immunology Project Consortium and the developers of the ImmPort data repository, we are developing and evaluating an end-to-end solution to the problems of metadata authoring and management that will generalize to other data-management environments. ? The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
- Published
- 2015
- Full Text
- View/download PDF
26. smartAPI: Towards a More Intelligent Network of Web APIs
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Dumontier, M., Dastgheib, S., Whetzel, T., Assisi, P., Aviilach, P., Jagodnik, K., Korodi, G., Pilarczyk, M., Schurer, S., Terryn, R., Verborgh, Ruben, and Wu, C.
- Published
- 2017
27. RDFox: a highly-scalable RDF store
- Author
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Nenov, Y, Piro, R, Motik, B, Horrocks, I, Wu, Z, Banerjee, J, Arenas, M, Corcho, Ó, Simperl, E, Strohmaier, M, d'Aquin, M, Srinivas, K, Groth, PT, Dumontier, M, Heflin, J, Thirunarayan, K, Staab, S, Arenas, M, Corcho, Ó, Simperl, E, Strohmaier, M, d'Aquin, M, Srinivas, K, Groth, P, Dumontier, M, Heflin, J, Thirunarayan, K, and Staab, S
- Subjects
Theoretical computer science ,Computer science ,Search engine indexing ,Scalability ,Systems architecture ,SPARQL ,Byte ,computer.file_format ,RDF ,Data structure ,computer ,Datalog ,computer.programming_language - Abstract
We present RDFox—a main-memory, scalable, centralised RDF store that supports materialisation-based parallel datalog reasoning and SPARQL query answering. RDFox uses novel and highlyefficient parallel reasoning algorithms for the computation and incremental update of datalog materialisations with efficient handling of owl: sameAs. In this system description paper, we present an overview of the system architecture and highlight the main ideas behind our indexing data structures and our novel reasoning algorithms. In addition, we evaluate RDFox on a high-end SPARC T5-8 server with 128 physical cores and 4TB of RAM. Our results show that RDFox can effectively exploit such a machine, achieving speedups of up to 87 times, storage of up to 9.2 billion triples, memory usage as low as 36.9 bytes per triple, importation rates of up to 1 million triples per second, and reasoning rates of up to 6.1 million triples per second.
- Published
- 2015
28. The smartAPI ecosystem for making Web APIs FAIR
- Author
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Dastgheib, S., Dastgheib, S., Whetzel, T., Zaveri, A., Afrasiabe, C., Assis, P., Availlach, P., Jagodnik, K., Korodi, G., Pilarczyk, M., De Pons, J., Schürer, S., Terryn, R., Verborgh, Ruben, Wu, C, Dumontier, M., Dastgheib, S., Dastgheib, S., Whetzel, T., Zaveri, A., Afrasiabe, C., Assis, P., Availlach, P., Jagodnik, K., Korodi, G., Pilarczyk, M., De Pons, J., Schürer, S., Terryn, R., Verborgh, Ruben, Wu, C, and Dumontier, M.
- Published
- 2017
29. Is Crowdsourcing Patient-Reported Outcomes the Future of Evidence-Based Medicine? A Case Study of Back Pain
- Author
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Peleg, M., Leung, T.I., Desai, M., Dumontier, M., Peleg, M., Leung, T.I., Desai, M., and Dumontier, M.
- Abstract
Evidence is lacking for patient-reported effectiveness of treatments for most medical conditions and specifically for lower back pain. In this paper, we examined a consumer-based social network that collects patients' treatment ratings as a potential source of evidence. Acknowledging the potential biases of this data set, we used propensity score matching and generalized linear regression to account for confounding variables. To evaluate validity, we compared results obtained by analyzing the patient reported data to results of evidence-based studies. Overall, there was agreement on the relationship between back pain and being obese. In addition, there was agreement about which treatments were effective or had no benefit. The patients' ratings also point to new evidence that postural modification treatment is effective and that surgery is harmful to a large proportion of patients.
- Published
- 2017
30. Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud
- Author
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Mons, B., Neylon, Cameron, Velterop, J., Dumontier, M., Da Silva Santos, L., Wilkinson, M., Mons, B., Neylon, Cameron, Velterop, J., Dumontier, M., Da Silva Santos, L., and Wilkinson, M.
- Abstract
The FAIR Data Principles propose that all scholarly output should be Findable, Accessible, Interoperable, and Reusable. As a set of guiding principles, expressing only the kinds of behaviours that researchers should expect from contemporary data resources, how the FAIR principles should manifest in reality was largely open to interpretation. As support for the Principles has spread, so has the breadth of these interpretations. In observing this creeping spread of interpretation, several of the original authors felt it was now appropriate to revisit the Principles, to clarify both what FAIRness is, and is not.
- Published
- 2017
31. The Ontology for Biomedical Investigations
- Author
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Bandrowski, A, Brinkman, R, Brochhausen, M, Brush, MH, Bug, B, Chibucos, MC, Clancy, K, Courtot, M, Derom, D, Dumontier, M, Fan, L, Fostel, J, Fragoso, G, Gibson, F, Gonzalez-Beltran, A, Haendel, MA, He, Y, Heiskanen, M, Hernandez-Boussard, T, Jensen, M, Lin, Y, Lister, AL, Lord, P, Malone, J, Manduchi, E, McGee, M, Morrison, N, Overton, JA, Parkinson, H, Peters, B, Rocca-Serra, P, Ruttenberg, A, Sansone, S-A, Scheuermann, RH, Schober, D, Smith, B, Soldatova, LN, Stoeckert, CJ, Taylor, CF, Torniai, C, Turner, JA, Vita, R, Whetzel, PL, Zheng, J, Xue, Y, and Xue, Y
- Subjects
0301 basic medicine ,Databases, Factual ,Microarrays ,Computer science ,Interoperability ,Social Sciences ,lcsh:Medicine ,Ontology (information science) ,Bioinformatics ,0302 clinical medicine ,Sociology ,Consortia ,Medicine and Health Sciences ,Psychology ,ontologies ,lcsh:Science ,Data Management ,Language ,computer.programming_language ,Protozoans ,Multidisciplinary ,Malarial Parasites ,Web Ontology Language ,Genomics ,Semantics ,Functional Genomics ,3. Good health ,ontology for Biomedical Investigations (OBI) ,Bioassays and Physiological Analysis ,Vertebrates ,web ontology language ,Ontology ,Web resource ,Research Article ,biomedical investigations ,Computer and Information Sciences ,Research and Analysis Methods ,Basic Formal Ontology ,Birds ,World Wide Web ,Open Biomedical Ontologies ,03 medical and health sciences ,Text mining ,Ontologies ,Parasitic Diseases ,Genetics ,Animals ,Humans ,Ontology for Biomedical Investigations ,Internet ,Metadata ,Raptors ,business.industry ,lcsh:R ,Organisms ,Cognitive Psychology ,Computational Biology ,Biology and Life Sciences ,Biological Ontologies ,Parasitic Protozoans ,Owls ,030104 developmental biology ,Amniotes ,Cognitive Science ,lcsh:Q ,business ,computer ,Software ,030217 neurology & neurosurgery ,Neuroscience - Abstract
The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl. This work was supported by the National Institute of Allergy and Infectious Diseases (https:// www.niaid.nih.gov/Pages/default.aspx) R01AI081062 to YH, the National Institutes of Health (http://www. nih.gov/) R01GM093132, U01 DK 072473, P41 HG003619 and HHSN272201400030C to CJS, HHSN272201400028C to RHS, HHSN272201200010C and 1U19AI118626 to BP, the California Institute for Regenerative Medicine (https:// www.cirm.ca.gov) GC1R-06673-B to RHS, the National Science Foundation Division of Biological Infrastructure (http://www.nsf.gov) 1458400 to MCC
- Published
- 2016
32. Experimental study of mass transport in PEMFCs: Through plane permeability and molecular diffusivity in GDLs
- Author
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Pant, L. M., Mitra, S., Carrigy, N., Secanell, M., Mangal, P., Zingan, V., and Dumontier, M
- Published
- 2015
- Full Text
- View/download PDF
33. Making linked data SPARQL with the InterMine biological data warehouse
- Author
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Déraspe, M., Binkley, G., Butano, D., Chadwick, M., Cherry, J.M., Clark-Casey, J., Contrino, S., Corbeil, Jacques, Heimbach, J., Karra, K., Lyne, R., Sullivan, J., Yehudi, Y., Micklem, G., Dumontier, M., Déraspe, M., Binkley, G., Butano, D., Chadwick, M., Cherry, J.M., Clark-Casey, J., Contrino, S., Corbeil, Jacques, Heimbach, J., Karra, K., Lyne, R., Sullivan, J., Yehudi, Y., Micklem, G., and Dumontier, M.
- Abstract
InterMine is a system for integrating, analysing, and republishing biological data from multiple sources. It provides access to these data via a web user interface and programmatic web services. However, the precise invocation of services and subsequent exploration of returned data require substantial expertise on the structure of the underlying database. Here, we describe an approach that uses Semantic Web technologies to make InterMine data more broadly accessible and reusable, in accordance with the FAIR principles. We describe a pipeline to extract, transform, and load a Linked Data representation of the InterMine store. We use Docker to bring together SPARQL-aware applications to search, browse, explore, and query the InterMine-based data. Our work therefore extends interoperability of the InterMine platform, and supports new query functionality across InterMine installations and the network of open Linked Data.
- Published
- 2016
34. Thematic issue of the Second combined Bio-ontologies and Phenotypes Workshop
- Author
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Verspoor, K, Oellrich, A, Collier, N, Groza, T, Rocca-Serra, P, Soldatova, L, Dumontier, M, Shah, N, Verspoor, K, Oellrich, A, Collier, N, Groza, T, Rocca-Serra, P, Soldatova, L, Dumontier, M, and Shah, N
- Abstract
© 2016 The Author(s). This special issue covers selected papers from the 18th Bio-Ontologies Special Interest Group meeting and Phenotype Day, which took place at the Intelligent Systems for Molecular Biology (ISMB) conference in Dublin in 2015. The papers presented in this collection range from descriptions of software tools supporting ontology development and annotation of objects with ontology terms, to applications of text mining for structured relation extraction involving diseases and phenotypes, to detailed proposals for new ontologies and mapping of existing ontologies. Together, the papers consider a range of representational issues in bio-ontology development, and demonstrate the applicability of bio-ontologies to support biological and clinical knowledge-based decision making and analysis. The full set of papers in the Thematic Issue is available at http://www.biomedcentral.com/collections/sig.
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- 2016
35. The health care and life sciences community profile for dataset descriptions
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Dumontier, M. (Michel), Gray, A.J.G. (Alasdair J.G.), Marshall, M.S. (M. Scott), Alexiev, V. (Vladimir), Ansell, P. (Peter), Bader, G. (Gary), Baran, J. (Joachim), Bolleman, J.T. (Jerven T.), Callahan, A. (Alison), Cruz-Toledo, J. (José), Gaudet, P. (Pascale), Gombocz, E.A. (Erich A.), Gonzalez-Beltran, A.N. (Alejandra N.), Groth, P. (Paul), Haendel, M. (Melissa), Ito, M. (Maori), Jupp, S. (Simon), Juty, N. (Nick), Katayama, T. (Toshiaki), Kobayashi, N. (Norio), Krishnaswami, K. (Kalpana), Laibe, C. (Camille), Le Novère, N. (Nicolas), Lin, S. (Simon), Malone, J. (James), Miller, M. (Michael), Mungall, C.J. (Christopher J.), Rietveld, L. (Laurens), Wimalaratne, S.M. (Sarala M.), Yamaguchi, A. (Atsuko), Dumontier, M. (Michel), Gray, A.J.G. (Alasdair J.G.), Marshall, M.S. (M. Scott), Alexiev, V. (Vladimir), Ansell, P. (Peter), Bader, G. (Gary), Baran, J. (Joachim), Bolleman, J.T. (Jerven T.), Callahan, A. (Alison), Cruz-Toledo, J. (José), Gaudet, P. (Pascale), Gombocz, E.A. (Erich A.), Gonzalez-Beltran, A.N. (Alejandra N.), Groth, P. (Paul), Haendel, M. (Melissa), Ito, M. (Maori), Jupp, S. (Simon), Juty, N. (Nick), Katayama, T. (Toshiaki), Kobayashi, N. (Norio), Krishnaswami, K. (Kalpana), Laibe, C. (Camille), Le Novère, N. (Nicolas), Lin, S. (Simon), Malone, J. (James), Miller, M. (Michael), Mungall, C.J. (Christopher J.), Rietveld, L. (Laurens), Wimalaratne, S.M. (Sarala M.), and Yamaguchi, A. (Atsuko)
- Abstract
Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.
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- 2016
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36. The FAIR Guiding Principles for scientific data management and stewardship
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Wilkinson, J.M. (Mark), Dumontier, M. (Michel), Aalbersberg, I.J. (Ijsbrand Jan), Appleton, G. (Gabrielle), Axton, M. (Myles), Baak, A. (Arie), Blomberg, N. (Niklas), Boiten, J.W. (Jan-Willem), Silva Santos, L.B. (Luiz Bonino) da, Bourne, P.E. (Philip), Bouwman, J. (Jildau), Brookes, A.J. (Anthony), Clark, T. (Tim), Crosas, M. (Mercè), Dillo, I. (Ingrid), Dumon, O. (Olivier), Edmunts, S. (Scott), Evelo, C.T. (Chris), Finkers, R. (Richard), Gonzalez-Beltran, A. (Alejandra), Gray, A. (Alastair), Groth, P. (Paul), Goble, C.A. (Carole Ann), Grethe, S. (Jeffrey), Heringa, J. (Jaap), Hoen, P.A.C. (Peter) 't, Hooft, R. (Rob), Kuhn, T. (Tobias), Kok, R. (Ruben), Kok, J. (Joost), Lusher, S.J. (Scott), Martone, M.E. (Maryann), Mons, A. (Albert), Packer, A. (Abel), Persson, B. (Bengt), Roca-Serra, P. (Philippe), Roos, M. (Marco), Schaik, R. (Rene) van, Sansone, S.A. (Susanna-Assunta), Schultes, E. (Erik), Sengstag, T. (Thierry), Slater, T. (Ted), Strawn, G. (George), Swertz, M. (Morris), Thompson, M. (Mark), Lei, J. (Johan) van der, Mulligen, E.M. (Erik) van, Velterop, J. (Jan), Waagmeester, A. (Andra), Wittenburg, P. (Peter), Wolstencroft, K. (Katherine), Zhao, J. (Jun), Mons, B. (Barend), Wilkinson, J.M. (Mark), Dumontier, M. (Michel), Aalbersberg, I.J. (Ijsbrand Jan), Appleton, G. (Gabrielle), Axton, M. (Myles), Baak, A. (Arie), Blomberg, N. (Niklas), Boiten, J.W. (Jan-Willem), Silva Santos, L.B. (Luiz Bonino) da, Bourne, P.E. (Philip), Bouwman, J. (Jildau), Brookes, A.J. (Anthony), Clark, T. (Tim), Crosas, M. (Mercè), Dillo, I. (Ingrid), Dumon, O. (Olivier), Edmunts, S. (Scott), Evelo, C.T. (Chris), Finkers, R. (Richard), Gonzalez-Beltran, A. (Alejandra), Gray, A. (Alastair), Groth, P. (Paul), Goble, C.A. (Carole Ann), Grethe, S. (Jeffrey), Heringa, J. (Jaap), Hoen, P.A.C. (Peter) 't, Hooft, R. (Rob), Kuhn, T. (Tobias), Kok, R. (Ruben), Kok, J. (Joost), Lusher, S.J. (Scott), Martone, M.E. (Maryann), Mons, A. (Albert), Packer, A. (Abel), Persson, B. (Bengt), Roca-Serra, P. (Philippe), Roos, M. (Marco), Schaik, R. (Rene) van, Sansone, S.A. (Susanna-Assunta), Schultes, E. (Erik), Sengstag, T. (Thierry), Slater, T. (Ted), Strawn, G. (George), Swertz, M. (Morris), Thompson, M. (Mark), Lei, J. (Johan) van der, Mulligen, E.M. (Erik) van, Velterop, J. (Jan), Waagmeester, A. (Andra), Wittenburg, P. (Peter), Wolstencroft, K. (Katherine), Zhao, J. (Jun), and Mons, B. (Barend)
- Abstract
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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- 2016
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37. Comment: The FAIR Guiding Principles for scientific data management and stewardship
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Wilkinson, M D, Dumontier, M, Aalbersberg, I J, Appleton, G, Axton, M, Baak, A, Blomberg, N, Boiten, J W, Santos, L B D, Bourne, P E, Bouwman, J, Brookes, AJ, Clark, T, Crosas, M, Dillo, I, Dumon, O, Edmunds, S, Evelo, C T, Finkers, R, Gonzalez-Beltran, A, Gray, A J G, Groth, P, Goble, C, Grethe, J S, Heringa, J, 't Hoen, PAC, Hooft, R, Kuhn, T, Kok, R, Kok, J, Lusher, SJ, Martone, M E, Mons, A, Packer, A L, Persson, B, Rocca-Serra, P, Roos, M, van Schaik, R, Sansone, S A, Schultes, E, Sengstag, T, Slater, T, Strawn, G, Swertz, MA, Thompson, M, Lei, Johan, van Mulligen, Erik, Velterop, J, Waagmeester, A, Wittenburg, P, Wolstencroft, K, Zhao, J, Mons, B, Wilkinson, M D, Dumontier, M, Aalbersberg, I J, Appleton, G, Axton, M, Baak, A, Blomberg, N, Boiten, J W, Santos, L B D, Bourne, P E, Bouwman, J, Brookes, AJ, Clark, T, Crosas, M, Dillo, I, Dumon, O, Edmunds, S, Evelo, C T, Finkers, R, Gonzalez-Beltran, A, Gray, A J G, Groth, P, Goble, C, Grethe, J S, Heringa, J, 't Hoen, PAC, Hooft, R, Kuhn, T, Kok, R, Kok, J, Lusher, SJ, Martone, M E, Mons, A, Packer, A L, Persson, B, Rocca-Serra, P, Roos, M, van Schaik, R, Sansone, S A, Schultes, E, Sengstag, T, Slater, T, Strawn, G, Swertz, MA, Thompson, M, Lei, Johan, van Mulligen, Erik, Velterop, J, Waagmeester, A, Wittenburg, P, Wolstencroft, K, Zhao, J, and Mons, B
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- 2016
38. The Bayesian ontology reasoner is born!
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Dumontier, M, Glimm, B, Gonçalves, RS, Horridge, M, Jiménez-Ruiz, E, Matentzoglu, N, Parsia, B, Stamou, GB, Stoilos, G, Ceylan, I, Mendez, J, Peñaloza, R, Dumontier, M, Glimm, B, Gonçalves, RS, Horridge, M, Jiménez-Ruiz, E, Matentzoglu, N, Parsia, B, Stamou, GB, Stoilos, G, Ceylan, I, Mendez, J, and Peñaloza, R
- Abstract
Bayesian ontology languages are a family of probabilistic ontology languages that allow to encode probabilistic information over the axioms of an ontology with the help of a Bayesian network. The Bayesian ontology language BEL is an extension of the lightweight Description Logic (DL) EL within the above-mentioned framework. We present the system BORN that implements the probabilistic subsumption problem for BEL.
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- 2015
39. Achieving human and machine accessibility of cited data in scholarly publications
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Starr, J. (Joan), Castro, E. (Eleni), Crosas, M. (Mercè), Dumontier, M. (Michel), Downs, R. R. (Robert), Duerr, R. (Ruth), Haak, L. L. (Laurel), Haendel, M. (Melissa), Herman, I. (Ivan), Hodson, S. (Simon), Hourclé, J. (Joe), Kratz, J.E. (John Ernest), Lin, J. (Jennifer), Nielsen, L.H. (Lars Holm), Nurnberger, A. (Amy), Proell, S. (Stefan), Rauber, A. (Andreas), Sacchi, S. (Simone), Smith, A. (Arthur), Taylor, M. (Mike), Clark, T. (Tim), Starr, J. (Joan), Castro, E. (Eleni), Crosas, M. (Mercè), Dumontier, M. (Michel), Downs, R. R. (Robert), Duerr, R. (Ruth), Haak, L. L. (Laurel), Haendel, M. (Melissa), Herman, I. (Ivan), Hodson, S. (Simon), Hourclé, J. (Joe), Kratz, J.E. (John Ernest), Lin, J. (Jennifer), Nielsen, L.H. (Lars Holm), Nurnberger, A. (Amy), Proell, S. (Stefan), Rauber, A. (Andreas), Sacchi, S. (Simone), Smith, A. (Arthur), Taylor, M. (Mike), and Clark, T. (Tim)
- Abstract
Reproducibility and reusability of research results is an important concern in scientific communication and science policy. A foundational element of reproducibility and reusability is the open and persistently available presentation of research data. However, many common approaches for primary data publication in use today do not achieve sufficient long-term robustness, openness, accessibility or uniformity. Nor do they permit comprehensive exploitation by modern Web technologies. This has led to several authoritative studies recommending uniform direct citation of data archived in persistent repositories. Data are to be considered as first-class scholarly objects, and treated similarly in many ways to cited and archived scientific and scholarly literature. Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP). We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data. The main target audience for the common implementation guidelines in this article consists of publishers, scholarly organizations, and persistent data repositories, including technical staff members in these organizations. But ordinary researchers can also benefit from these recommendations. The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data.
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- 2015
40. Publishing without Publishers: a Decentralized Approach to Dissemination, Retrieval, and Archiving of Data
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Kuhn, T., Chichester, C., Krauthammer, M., Dumontier, M., Kuhn, T., Chichester, C., Krauthammer, M., and Dumontier, M.
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Making available and archiving scientific results is for the most part still considered the task of classical publishing companies, despite the fact that classical forms of publishing centered around printed narrative articles no longer seem well-suited in the digital age. In particular, there exist currently no efficient, reliable, and agreed-upon methods for publishing scientific datasets, which have become increasingly important for science. Here we propose to design scientific data publishing as a Web-based bottom-up process, without top-down control of central authorities such as publishing companies. Based on a novel combination of existing concepts and technologies, we present a server network to decentrally store and archive data in the form of nanopublications, an RDF-based format to represent scientific data. We show how this approach allows researchers to publish, retrieve, verify, and recombine datasets of nanopublications in a reliable and trustworthy manner, and we argue that this architecture could be used for the Semantic Web in general. Evaluation of the current small network shows that this system is efficient and reliable.
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- 2015
41. Making Digital Artifacts on the Web Verifiable and Reliable
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Kuhn, T., Dumontier, M., Kuhn, T., and Dumontier, M.
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The current Web has no general mechanisms to make digital artifacts - such as datasets, code, texts, and images - verifiable and permanent. For digital artifacts that are supposed to be immutable, there is moreover no commonly accepted method to enforce this immutability. These shortcomings have a serious negative impact on the ability to reproduce the results of processes that rely on Web resources, which in turn heavily impacts areas such as science where reproducibility is important. To solve this problem, we propose trusty URIs containing cryptographic hash values. We show how trusty URIs can be used for the verification of digital artifacts, in a manner that is independent of the serialization format in the case of structured data files such as nanopublications. We demonstrate how the contents of these files become immutable, including dependencies to external digital artifacts and thereby extending the range of verifiability to the entire reference tree. Our approach sticks to the core principles of the Web, namely openness and decentralized architecture, and is fully compatible with existing standards and protocols. Evaluation of our reference implementations shows that these design goals are indeed accomplished by our approach, and that it remains practical even for very large files.
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- 2015
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42. Provenance-Centered Dataset of Drug-Drug Interactions
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Banda, J.M., Kuhn, T., Shah, N.H., Dumontier, M., Banda, J.M., Kuhn, T., Shah, N.H., and Dumontier, M.
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Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records, public databases (Drugbank), etc. While each one of these approaches is properly statistically validated, they do not take into consideration the overlap between them as one of their decision making variables. In this paper we present LInked Drug-Drug Interactions (LIDDI), a public nanopublication-based RDF dataset with trusty URIs that encompasses some of the most cited prediction methods and sources to provide researchers a resource for leveraging the work of others into their prediction methods. As one of the main issues to overcome the usage of external resources is their mappings between drug names and identifiers used, we also provide the set of mappings we curated to be able to compare the multiple sources we aggregate in our dataset.
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- 2015
43. Evaluating a variety of text-mined features for automatic protein function prediction
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Shah, N, Dumontier, M, Soldatova, L, Rocca-Serra, P, Funk, C, Kahanda, I, Ben-Hur, A, VERSPOOR, CM, Shah, N, Dumontier, M, Soldatova, L, Rocca-Serra, P, Funk, C, Kahanda, I, Ben-Hur, A, and VERSPOOR, CM
- Abstract
Most computational methods that predict protein function do not take advantage of the large amount of information contained in the biomedical literature. In this work we evaluate both ontology term co-mention and bag-of-words features mined from the biomedical literature and analyze their impact in the context of a structured output support vector machine model, GOstruct. We find that even simple literature based features are useful for predicting human protein function (F-max: Molecular Function =0.408, Biological Process =0.461, Cellular Component =0.608). One advantage of using literature features is their ability to offer easy verification of automated predictions. We find through manual inspection of misclassifications that some false positive predictions could be biologically valid predictions based upon support extracted from the literature. Additionally, we present a "medium-throughput" pipeline that was used to annotate a large subset of co-mentions; we suggest that this strategy could help to speed up the rate at which proteins are curated.
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- 2015
44. Interoperability of text corpus annotations with the semantic web
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Verspoor, K, Kim, J-D, Dumontier, M, Verspoor, K, Kim, J-D, and Dumontier, M
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- 2015
45. Special issue on bio-ontologies and phenotypes
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Soldatova, LN, Collier, N, Oellrich, A, Groza, T, Verspoor, K, Rocca-Serra, P, Dumontier, M, Shah, NH, Soldatova, LN, Collier, N, Oellrich, A, Groza, T, Verspoor, K, Rocca-Serra, P, Dumontier, M, and Shah, NH
- Abstract
The bio-ontologies and phenotypes special issue includes eight papers selected from the 11 papers presented at the Bio-Ontologies SIG (Special Interest Group) and the Phenotype Day at ISMB (Intelligent Systems for Molecular Biology) conference in Boston in 2014. The selected papers span a wide range of topics including the automated re-use and update of ontologies, quality assessment of ontological resources, and the systematic description of phenotype variation, driven by manual, semi- and fully automatic means.
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- 2015
46. Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies
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Samwald, M, Giménez, JAM, Boyce, RD, Freimuth, RR, Adlassnig, KP, Dumontier, M, Samwald, M, Giménez, JAM, Boyce, RD, Freimuth, RR, Adlassnig, KP, and Dumontier, M
- Abstract
Background: Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics. Methods: We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles. Results: Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners. Conclusions: The ontology-based framework we developed can be used to represent, orga
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- 2015
47. Klink-2: Integrating multiple web sources to generate semantic topic networks
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Arenas, M, Corcho, O, Simperl, E, Strohmaier, M, d’Aquin, M, Srinivas, K, Groth, P, Dumontier, M, Heflin, J, Thirunarayan, K, Staab, S, Osborne, F, Motta, E, Osborne F, Motta E, Arenas, M, Corcho, O, Simperl, E, Strohmaier, M, d’Aquin, M, Srinivas, K, Groth, P, Dumontier, M, Heflin, J, Thirunarayan, K, Staab, S, Osborne, F, Motta, E, Osborne F, and Motta E
- Abstract
The amount of scholarly data available on the web is steadily increasing, enabling different types of analytics which can provide important insights into the research activity. In order to make sense of and explore this large-scale body of knowledge we need an accurate, comprehensive and up-to-date ontology of research topics. Unfortunately, human crafted classifications do not satisfy these criteria, as they evolve too slowly and tend to be too coarse-grained. Current automated methods for generating ontologies of research areas also present a number of limitations, such as: i) they do not consider the rich amount of indirect statistical and semantic relationships, which can help to understand the relation between two topics – e.g., the fact that two research areas are associated with a similar set of venues or technologies; ii) they do not distinguish between different kinds of hierarchical relationships; and iii) they are not able to handle effectively ambiguous topics characterized by a noisy set of relationships. In this paper we present Klink-2, a novel approach which improves on our earlier work on automatic generation of semantic topic networks and addresses the aforementioned limitations by taking advantage of a variety of knowledge sources available on the web. In particular, Klink-2 analyses networks of research entities (including papers, authors, venues, and technologies) to infer three kinds of semantic relationships between topics. It also identifies ambiguous keywords (e.g., “ontology”) and separates them into the appropriate distinct topics – e.g., “ontology/philosophy” vs. “ontology/semantic web”. Our experimental evaluation shows that the ability of Klink-2 to integrate a high number of data sources and to generate topics with accurate contextual meaning yields significant improvements over other algorithms in terms of both precision and recall.
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- 2015
48. Xenoestrogens disrupt type 2 collagen expression in chondrocytes in vitro.
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Corvol, M.T., primary, Auxietre, T., additional, Dumontier, M.-F., additional, Kellerman, O., additional, and Savouret, J.-F., additional
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- 2015
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49. Realism for scientific ontologies
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Dumontier, M., Hoehndorf, Robert, Galton, A., and Mizoguchi, R.
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- 2010
50. Trusty URIs: Verifiable, immutable, and permanent digital artifacts for linked data
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Kuhn, Tobias, Dumontier, M., Kuhn, Tobias, and Dumontier, M.
- Abstract
To make digital resources on the web verifiable, immutable, and permanent, we propose a technique to include cryptographic hash values in uris. We call them trusty uris and we show how they can be used for approaches like nanopublications to make not only specific resources but their entire reference trees verifiable. Digital artifacts can be identified not only on the byte level but on more abstract levels such as rdf graphs, which means that resources keep their hash values even when presented in a different format. Our approach sticks to the core principles of the web, namely openness and decentralized architecture, is fully compatible with existing standards and protocols, and can therefore be used right away. Evaluation of our reference implementations shows that these desired properties are indeed accomplished by our approach, and that it remains practical even for very large files.
- Published
- 2014
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