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643 results on '"Reinders, Marcel J. T."'

1. PATE: Proximity-Aware Time series anomaly Evaluation

Author

Ghorbani, Ramin, Reinders, Marcel J. T., and Tax, David M. J.

Subjects
Computer Science - Machine Learning
Abstract

Evaluating anomaly detection algorithms in time series data is critical as inaccuracies can lead to flawed decision-making in various domains where real-time analytics and data-driven strategies are essential. Traditional performance metrics assume iid data and fail to capture the complex temporal dynamics and specific characteristics of time series anomalies, such as early and delayed detections. We introduce Proximity-Aware Time series anomaly Evaluation (PATE), a novel evaluation metric that incorporates the temporal relationship between prediction and anomaly intervals. PATE uses proximity-based weighting considering buffer zones around anomaly intervals, enabling a more detailed and informed assessment of a detection. Using these weights, PATE computes a weighted version of the area under the Precision and Recall curve. Our experiments with synthetic and real-world datasets show the superiority of PATE in providing more sensible and accurate evaluations than other evaluation metrics. We also tested several state-of-the-art anomaly detectors across various benchmark datasets using the PATE evaluation scheme. The results show that a common metric like Point-Adjusted F1 Score fails to characterize the detection performances well, and that PATE is able to provide a more fair model comparison. By introducing PATE, we redefine the understanding of model efficacy that steers future studies toward developing more effective and accurate detection models., Comment: Accepted by ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2024), Research Track. (Preprint version)

Published
2024

2. RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detection

Author

Ghorbani, Ramin, Reinders, Marcel J. T., and Tax, David M. J.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Anomaly detection in time series data is crucial across various domains. The scarcity of labeled data for such tasks has increased the attention towards unsupervised learning methods. These approaches, often relying solely on reconstruction error, typically fail to detect subtle anomalies in complex datasets. To address this, we introduce RESTAD, an adaptation of the Transformer model by incorporating a layer of Radial Basis Function (RBF) neurons within its architecture. This layer fits a non-parametric density in the latent representation, such that a high RBF output indicates similarity with predominantly normal training data. RESTAD integrates the RBF similarity scores with the reconstruction errors to increase sensitivity to anomalies. Our empirical evaluations demonstrate that RESTAD outperforms various established baselines across multiple benchmark datasets., Comment: Manuscript under review

Published
2024

4. Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification

Author

Ghorbani, Ramin, Reinders, Marcel J. T., and Tax, David M. J.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Photoplethysmography (PPG) signals, typically acquired from wearable devices, hold significant potential for continuous fitness-health monitoring. In particular, heart conditions that manifest in rare and subtle deviating heart patterns may be interesting. However, robust and reliable anomaly detection within these data remains a challenge due to the scarcity of labeled data and high inter-subject variability. This paper introduces a two-stage framework leveraging representation learning and personalization to improve anomaly detection performance in PPG data. The proposed framework first employs representation learning to transform the original PPG signals into a more discriminative and compact representation. We then apply three different unsupervised anomaly detection methods for movement detection and biometric identification. We validate our approach using two different datasets in both generalized and personalized scenarios. The results show that representation learning significantly improves anomaly detection performance while reducing the high inter-subject variability. Personalized models further enhance anomaly detection performance, underscoring the role of personalization in PPG-based fitness-health monitoring systems. The results from biometric identification show that it's easier to distinguish a new user from one intended authorized user than from a group of users. Overall, this study provides evidence of the effectiveness of representation learning and personalization for anomaly detection in PPG data.

Published
2023

6. Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability

Author

Ghorbani, Ramin, Reinders, Marcel J. T., and Tax, David M. J.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

With the progress of sensor technology in wearables, the collection and analysis of PPG signals are gaining more interest. Using Machine Learning, the cardiac rhythm corresponding to PPG signals can be used to predict different tasks such as activity recognition, sleep stage detection, or more general health status. However, supervised learning is often limited by the amount of available labeled data, which is typically expensive to obtain. To address this problem, we propose a Self-Supervised Learning (SSL) method with a pretext task of signal reconstruction to learn an informative generalized PPG representation. The performance of the proposed SSL framework is compared with two fully supervised baselines. The results show that in a very limited label data setting (10 samples per class or less), using SSL is beneficial, and a simple classifier trained on SSL-learned representations outperforms fully supervised deep neural networks. However, the results reveal that the SSL-learned representations are too focused on encoding the subjects. Unfortunately, there is high inter-subject variability in the SSL-learned representations, which makes working with this data more challenging when labeled data is scarce. The high inter-subject variability suggests that there is still room for improvements in learning representations. In general, the results suggest that SSL may pave the way for the broader use of machine learning models on PPG data in label-scarce regimes., Comment: The current version has been accepted to be presented at ICMLT 2023; Typo corrected in the second author's name

Published
2022

9. Percolate: An Exponential Family JIVE Model to Design DNA-Based Predictors of Drug Response

Author

Mourragui, Soufiane M. C., Loog, Marco, van Nee, Mirrelijn, de Wiel, Mark A van, Reinders, Marcel J. T., Wessels, Lodewyk F. A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Tang, Haixu, editor

Published
2023
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10. Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s disease

Author

Holstege, Henne, Hulsman, Marc, Charbonnier, Camille, Grenier-Boley, Benjamin, Quenez, Olivier, Grozeva, Detelina, van Rooij, Jeroen G. J., Sims, Rebecca, Ahmad, Shahzad, Amin, Najaf, Norsworthy, Penny J., Dols-Icardo, Oriol, Hummerich, Holger, Kawalia, Amit, Amouyel, Philippe, Beecham, Gary W., Berr, Claudine, Bis, Joshua C., Boland, Anne, Bossù, Paola, Bouwman, Femke, Bras, Jose, Campion, Dominique, Cochran, J. Nicholas, Daniele, Antonio, Dartigues, Jean-François, Debette, Stéphanie, Deleuze, Jean-François, Denning, Nicola, DeStefano, Anita L., Farrer, Lindsay A., Fernández, Maria Victoria, Fox, Nick C., Galimberti, Daniela, Genin, Emmanuelle, Gille, Johan J. P., Le Guen, Yann, Guerreiro, Rita, Haines, Jonathan L., Holmes, Clive, Ikram, M. Arfan, Ikram, M. Kamran, Jansen, Iris E., Kraaij, Robert, Lathrop, Marc, Lemstra, Afina W., Lleó, Alberto, Luckcuck, Lauren, Mannens, Marcel M. A. M., Marshall, Rachel, Martin, Eden R., Masullo, Carlo, Mayeux, Richard, Mecocci, Patrizia, Meggy, Alun, Mol, Merel O., Morgan, Kevin, Myers, Richard M., Nacmias, Benedetta, Naj, Adam C., Napolioni, Valerio, Pasquier, Florence, Pastor, Pau, Pericak-Vance, Margaret A., Raybould, Rachel, Redon, Richard, Reinders, Marcel J. T., Richard, Anne-Claire, Riedel-Heller, Steffi G., Rivadeneira, Fernando, Rousseau, Stéphane, Ryan, Natalie S., Saad, Salha, Sanchez-Juan, Pascual, Schellenberg, Gerard D., Scheltens, Philip, Schott, Jonathan M., Seripa, Davide, Seshadri, Sudha, Sie, Daoud, Sistermans, Erik A., Sorbi, Sandro, van Spaendonk, Resie, Spalletta, Gianfranco, Tesi, Niccolo’, Tijms, Betty, Uitterlinden, André G., van der Lee, Sven J., Visser, Pieter Jelle, Wagner, Michael, Wallon, David, Wang, Li-San, Zarea, Aline, Clarimon, Jordi, van Swieten, John C., Greicius, Michael D., Yokoyama, Jennifer S., Cruchaga, Carlos, Hardy, John, Ramirez, Alfredo, Mead, Simon, van der Flier, Wiesje M., van Duijn, Cornelia M., Williams, Julie, Nicolas, Gaël, Bellenguez, Céline, and Lambert, Jean-Charles

Published
2022
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13. New insights into the genetic etiology of Alzheimer’s disease and related dementias

Author

Bellenguez, Céline, Küçükali, Fahri, Jansen, Iris E., Kleineidam, Luca, Moreno-Grau, Sonia, Amin, Najaf, Naj, Adam C., Campos-Martin, Rafael, Grenier-Boley, Benjamin, Andrade, Victor, Holmans, Peter A., Boland, Anne, Damotte, Vincent, van der Lee, Sven J., Costa, Marcos R., Kuulasmaa, Teemu, Yang, Qiong, de Rojas, Itziar, Bis, Joshua C., Yaqub, Amber, Prokic, Ivana, Chapuis, Julien, Ahmad, Shahzad, Giedraitis, Vilmantas, Aarsland, Dag, Garcia-Gonzalez, Pablo, Abdelnour, Carla, Alarcón-Martín, Emilio, Alcolea, Daniel, Alegret, Montserrat, Alvarez, Ignacio, Álvarez, Victoria, Armstrong, Nicola J., Tsolaki, Anthoula, Antúnez, Carmen, Appollonio, Ildebrando, Arcaro, Marina, Archetti, Silvana, Pastor, Alfonso Arias, Arosio, Beatrice, Athanasiu, Lavinia, Bailly, Henri, Banaj, Nerisa, Baquero, Miquel, Barral, Sandra, Beiser, Alexa, Pastor, Ana Belén, Below, Jennifer E., Benchek, Penelope, Benussi, Luisa, Berr, Claudine, Besse, Céline, Bessi, Valentina, Binetti, Giuliano, Bizarro, Alessandra, Blesa, Rafael, Boada, Mercè, Boerwinkle, Eric, Borroni, Barbara, Boschi, Silvia, Bossù, Paola, Bråthen, Geir, Bressler, Jan, Bresner, Catherine, Brodaty, Henry, Brookes, Keeley J., Brusco, Luis Ignacio, Buiza-Rueda, Dolores, Bûrger, Katharina, Burholt, Vanessa, Bush, William S., Calero, Miguel, Cantwell, Laura B., Chene, Geneviève, Chung, Jaeyoon, Cuccaro, Michael L., Carracedo, Ángel, Cecchetti, Roberta, Cervera-Carles, Laura, Charbonnier, Camille, Chen, Hung-Hsin, Chillotti, Caterina, Ciccone, Simona, Claassen, Jurgen A. H. R., Clark, Christopher, Conti, Elisa, Corma-Gómez, Anaïs, Costantini, Emanuele, Custodero, Carlo, Daian, Delphine, Dalmasso, Maria Carolina, Daniele, Antonio, Dardiotis, Efthimios, Dartigues, Jean-François, de Deyn, Peter Paul, de Paiva Lopes, Katia, de Witte, Lot D., Debette, Stéphanie, Deckert, Jürgen, del Ser, Teodoro, Denning, Nicola, DeStefano, Anita, Dichgans, Martin, Diehl-Schmid, Janine, Diez-Fairen, Mónica, Rossi, Paolo Dionigi, Djurovic, Srdjan, Duron, Emmanuelle, Düzel, Emrah, Dufouil, Carole, Eiriksdottir, Gudny, Engelborghs, Sebastiaan, Escott-Price, Valentina, Espinosa, Ana, Ewers, Michael, Faber, Kelley M., Fabrizio, Tagliavini, Nielsen, Sune Fallgaard, Fardo, David W., Farotti, Lucia, Fenoglio, Chiara, Fernández-Fuertes, Marta, Ferrari, Raffaele, Ferreira, Catarina B., Ferri, Evelyn, Fin, Bertrand, Fischer, Peter, Fladby, Tormod, Fließbach, Klaus, Fongang, Bernard, Fornage, Myriam, Fortea, Juan, Foroud, Tatiana M., Fostinelli, Silvia, Fox, Nick C., Franco-Macías, Emlio, Bullido, María J., Frank-García, Ana, Froelich, Lutz, Fulton-Howard, Brian, Galimberti, Daniela, García-Alberca, Jose Maria, García-González, Pablo, Garcia-Madrona, Sebastian, Garcia-Ribas, Guillermo, Ghidoni, Roberta, Giegling, Ina, Giorgio, Giaccone, Goate, Alison M., Goldhardt, Oliver, Gomez-Fonseca, Duber, González-Pérez, Antonio, Graff, Caroline, Grande, Giulia, Green, Emma, Grimmer, Timo, Grünblatt, Edna, Grunin, Michelle, Gudnason, Vilmundur, Guetta-Baranes, Tamar, Haapasalo, Annakaisa, Hadjigeorgiou, Georgios, Haines, Jonathan L., Hamilton-Nelson, Kara L., Hampel, Harald, Hanon, Olivier, Hardy, John, Hartmann, Annette M., Hausner, Lucrezia, Harwood, Janet, Heilmann-Heimbach, Stefanie, Helisalmi, Seppo, Heneka, Michael T., Hernández, Isabel, Herrmann, Martin J., Hoffmann, Per, Holmes, Clive, Holstege, Henne, Vilas, Raquel Huerto, Hulsman, Marc, Humphrey, Jack, Biessels, Geert Jan, Jian, Xueqiu, Johansson, Charlotte, Jun, Gyungah R., Kastumata, Yuriko, Kauwe, John, Kehoe, Patrick G., Kilander, Lena, Ståhlbom, Anne Kinhult, Kivipelto, Miia, Koivisto, Anne, Kornhuber, Johannes, Kosmidis, Mary H., Kukull, Walter A., Kuksa, Pavel P., Kunkle, Brian W., Kuzma, Amanda B., Lage, Carmen, Laukka, Erika J., Launer, Lenore, Lauria, Alessandra, Lee, Chien-Yueh, Lehtisalo, Jenni, Lerch, Ondrej, Lleó, Alberto, Longstreth, Jr, William, Lopez, Oscar, de Munain, Adolfo Lopez, Love, Seth, Löwemark, Malin, Luckcuck, Lauren, Lunetta, Kathryn L., Ma, Yiyi, Macías, Juan, MacLeod, Catherine A., Maier, Wolfgang, Mangialasche, Francesca, Spallazzi, Marco, Marquié, Marta, Marshall, Rachel, Martin, Eden R., Montes, Angel Martín, Rodríguez, Carmen Martínez, Masullo, Carlo, Mayeux, Richard, Mead, Simon, Mecocci, Patrizia, Medina, Miguel, Meggy, Alun, Mehrabian, Shima, Mendoza, Silvia, Menéndez-González, Manuel, Mir, Pablo, Moebus, Susanne, Mol, Merel, Molina-Porcel, Laura, Montrreal, Laura, Morelli, Laura, Moreno, Fermin, Morgan, Kevin, Mosley, Thomas, Nöthen, Markus M., Muchnik, Carolina, Mukherjee, Shubhabrata, Nacmias, Benedetta, Ngandu, Tiia, Nicolas, Gael, Nordestgaard, Børge G., Olaso, Robert, Orellana, Adelina, Orsini, Michela, Ortega, Gemma, Padovani, Alessandro, Paolo, Caffarra, Papenberg, Goran, Parnetti, Lucilla, Pasquier, Florence, Pastor, Pau, Peloso, Gina, Pérez-Cordón, Alba, Pérez-Tur, Jordi, Pericard, Pierre, Peters, Oliver, Pijnenburg, Yolande A. L., Pineda, Juan A., Piñol-Ripoll, Gerard, Pisanu, Claudia, Polak, Thomas, Popp, Julius, Posthuma, Danielle, Priller, Josef, Puerta, Raquel, Quenez, Olivier, Quintela, Inés, Thomassen, Jesper Qvist, Rábano, Alberto, Rainero, Innocenzo, Rajabli, Farid, Ramakers, Inez, Real, Luis M., Reinders, Marcel J. T., Reitz, Christiane, Reyes-Dumeyer, Dolly, Ridge, Perry, Riedel-Heller, Steffi, Riederer, Peter, Roberto, Natalia, Rodriguez-Rodriguez, Eloy, Rongve, Arvid, Allende, Irene Rosas, Rosende-Roca, Maitée, Royo, Jose Luis, Rubino, Elisa, Rujescu, Dan, Sáez, María Eugenia, Sakka, Paraskevi, Saltvedt, Ingvild, Sanabria, Ángela, Sánchez-Arjona, María Bernal, Sanchez-Garcia, Florentino, Juan, Pascual Sánchez, Sánchez-Valle, Raquel, Sando, Sigrid B., Sarnowski, Chloé, Satizabal, Claudia L., Scamosci, Michela, Scarmeas, Nikolaos, Scarpini, Elio, Scheltens, Philip, Scherbaum, Norbert, Scherer, Martin, Schmid, Matthias, Schneider, Anja, Schott, Jonathan M., Selbæk, Geir, Seripa, Davide, Serrano, Manuel, Sha, Jin, Shadrin, Alexey A., Skrobot, Olivia, Slifer, Susan, Snijders, Gijsje J. L., Soininen, Hilkka, Solfrizzi, Vincenzo, Solomon, Alina, Song, Yeunjoo, Sorbi, Sandro, Sotolongo-Grau, Oscar, Spalletta, Gianfranco, Spottke, Annika, Squassina, Alessio, Stordal, Eystein, Tartan, Juan Pablo, Tárraga, Lluís, Tesí, Niccolo, Thalamuthu, Anbupalam, Thomas, Tegos, Tosto, Giuseppe, Traykov, Latchezar, Tremolizzo, Lucio, Tybjærg-Hansen, Anne, Uitterlinden, Andre, Ullgren, Abbe, Ulstein, Ingun, Valero, Sergi, Valladares, Otto, Broeckhoven, Christine Van, Vance, Jeffery, Vardarajan, Badri N., van der Lugt, Aad, Dongen, Jasper Van, van Rooij, Jeroen, van Swieten, John, Vandenberghe, Rik, Verhey, Frans, Vidal, Jean-Sébastien, Vogelgsang, Jonathan, Vyhnalek, Martin, Wagner, Michael, Wallon, David, Wang, Li-San, Wang, Ruiqi, Weinhold, Leonie, Wiltfang, Jens, Windle, Gill, Woods, Bob, Yannakoulia, Mary, Zare, Habil, Zhao, Yi, Zhang, Xiaoling, Zhu, Congcong, Zulaica, Miren, Farrer, Lindsay A., Psaty, Bruce M., Ghanbari, Mohsen, Raj, Towfique, Sachdev, Perminder, Mather, Karen, Jessen, Frank, Ikram, M. Arfan, de Mendonça, Alexandre, Hort, Jakub, Tsolaki, Magda, Pericak-Vance, Margaret A., Amouyel, Philippe, Williams, Julie, Frikke-Schmidt, Ruth, Clarimon, Jordi, Deleuze, Jean-François, Rossi, Giacomina, Seshadri, Sudha, Andreassen, Ole A., Ingelsson, Martin, Hiltunen, Mikko, Sleegers, Kristel, Schellenberg, Gerard D., van Duijn, Cornelia M., Sims, Rebecca, van der Flier, Wiesje M., Ruiz, Agustín, Ramirez, Alfredo, and Lambert, Jean-Charles

Published
2022
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14. Development and validation of an early warning model for hospitalized COVID-19 patients: a multi-center retrospective cohort study

Author

Smit, Jim M., Krijthe, Jesse H., Tintu, Andrei N., Endeman, Henrik, Ludikhuize, Jeroen, van Genderen, Michel E., Hassan, Shermarke, El Moussaoui, Rachida, Westerweel, Peter E., Goekoop, Robbert J., Waverijn, Geeke, Verheijen, Tim, den Hollander, Jan G., de Boer, Mark G. J., Gommers, Diederik A. M. P. J., van der Vlies, Robin, Schellings, Mark, Carels, Regina A., van Nieuwkoop, Cees, Arbous, Sesmu M., van Bommel, Jasper, Knevel, Rachel, de Rijke, Yolanda B., and Reinders, Marcel J. T.

Published
2022
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19. Systems analysis and controlled malaria infection in Europeans and Africans elucidate naturally acquired immunity

Author

de Jong, Sanne E., van Unen, Vincent, Manurung, Mikhael D., Stam, Koen A., Goeman, Jelle J., Jochems, Simon P., Höllt, Thomas, Pezzotti, Nicola, Mouwenda, Yoanne D., Betouke Ongwe, Madeleine Eunice, Lorenz, Freia-Raphaella, Kruize, Yvonne C. M., Azimi, Shohreh, König, Marion H., Vilanova, Anna, Eisemann, Elmar, Lelieveldt, Boudewijn P. F., Roestenberg, Meta, Sim, B. Kim Lee, Reinders, Marcel J. T., Fendel, Rolf, Hoffman, Stephen L., Kremsner, Peter G., Koning, Frits, Mordmüller, Benjamin, Lell, Bertrand, and Yazdanbakhsh, Maria

Published
2021
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21. TREM2 Burden associates solely with Alzheimer’s Disease, and decreases the Chance to become a Centenarian

Author

Dijkstra, Janna I.R., primary, Vermunt, Lisa, additional, Biessels, Geert Jan, additional, Braber, Anouk, additional, Claassen, Jurgen A.H.R., additional, De Deyn, Peter Paul, additional, Ghanbari, Mohsen, additional, Huisman, Martijn, additional, Hulsman, Marc, additional, Ramakers, Inez H.G.B., additional, Reinders, Marcel J. T., additional, Seelaar, Harro, additional, Tesi, Niccoló, additional, Visser, Pieter Jelle, additional, van der Flier, Wiesje M., additional, Teunissen, Charlotte E., additional, Holstege, Henne, additional, and Van Der Lee, Sven J, additional

Published
2023
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25. Machine learning–based biomarker profile derived from 4210 serially measured proteins predicts clinical outcome of patients with heart failure

Author

de Bakker, Marie, primary, Petersen, Teun B, additional, Rueten-Budde, Anja J, additional, Akkerhuis, K Martijn, additional, Umans, Victor A, additional, Brugts, Jasper J, additional, Germans, Tjeerd, additional, Reinders, Marcel J T, additional, Katsikis, Peter D, additional, van der Spek, Peter J, additional, Ostroff, Rachel, additional, She, Ruicong, additional, Lanfear, David, additional, Asselbergs, Folkert W, additional, Boersma, Eric, additional, Rizopoulos, Dimitris, additional, and Kardys, Isabella, additional

Published
2023
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26. Publisher Correction: A meta-analysis of genome-wide association studies identifies multiple longevity genes

Author

Deelen, Joris, Evans, Daniel S., Arking, Dan E., Tesi, Niccolò, Nygaard, Marianne, Liu, Xiaomin, Wojczynski, Mary K., Biggs, Mary L., van der Spek, Ashley, Atzmon, Gil, Ware, Erin B., Sarnowski, Chloé, Smith, Albert V., Seppälä, Ilkka, Cordell, Heather J., Dose, Janina, Amin, Najaf, Arnold, Alice M., Ayers, Kristin L., Barzilai, Nir, Becker, Elizabeth J., Beekman, Marian, Blanché, Hélène, Christensen, Kaare, Christiansen, Lene, Collerton, Joanna C., Cubaynes, Sarah, Cummings, Steven R., Davies, Karen, Debrabant, Birgit, Deleuze, Jean-François, Duncan, Rachel, Faul, Jessica D., Franceschi, Claudio, Galan, Pilar, Gudnason, Vilmundur, Harris, Tamara B., Huisman, Martijn, Hurme, Mikko A., Jagger, Carol, Jansen, Iris, Jylhä, Marja, Kähönen, Mika, Karasik, David, Kardia, Sharon L. R., Kingston, Andrew, Kirkwood, Thomas B. L., Launer, Lenore J., Lehtimäki, Terho, Lieb, Wolfgang, Lyytikäinen, Leo-Pekka, Martin-Ruiz, Carmen, Min, Junxia, Nebel, Almut, Newman, Anne B., Nie, Chao, Nohr, Ellen A., Orwoll, Eric S., Perls, Thomas T., Province, Michael A., Psaty, Bruce M., Raitakari, Olli T., Reinders, Marcel J. T., Robine, Jean-Marie, Rotter, Jerome I., Sebastiani, Paola, Smith, Jennifer, Sørensen, Thorkild I. A., Taylor, Kent D., Uitterlinden, André G., van der Flier, Wiesje, van der Lee, Sven J., van Duijn, Cornelia M., van Heemst, Diana, Vaupel, James W., Weir, David, Ye, Kenny, Zeng, Yi, Zheng, Wanlin, Holstege, Henne, Kiel, Douglas P., Lunetta, Kathryn L., Slagboom, P. Eline, and Murabito, Joanne M.

Published
2021
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28. Genome-wide association study of frontotemporal dementia identifies a C9ORF72 haplotype with a median of 12-G4C2 repeats that predisposes to pathological repeat expansions

Author

Reus, Lianne M., Jansen, Iris E., Mol, Merel O., van Ruissen, Fred, van Rooij, Jeroen, van Schoor, Natasja M., Tesi, Niccolò, Reinders, Marcel J. T., Huisman, Martijn A., Holstege, Henne, Visser, Pieter Jelle, de Boer, Sterre C. M., Hulsman, Marc, Ahmad, Shahzad, Amin, Najaf, Uitterlinden, Andre G., Ikram, Arfan, van Duijn, Cornelia M., Seelaar, Harro, Ramakers, Inez H. G. B., Verhey, Frans R. J., van der Lugt, Aad, Claassen, Jurgen A. H. R., Jan Biessels, Geert, De Deyn, Peter Paul, Scheltens, Philip, van der Flier, Wiesje M., van Swieten, John C., Pijnenburg, Yolande A. L., and van der Lee, Sven J.

Published
2021
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29. Prior Biological Knowledge And Epigenetic Information Enhances Prediction Accuracy Of Bayesian Wnt Pathway

Author

Sinha, Shriprakash, Reinders, Marcel J. T., and Verhaegh, Wim

Subjects
Quantitative Biology - Molecular Networks, Computer Science - Computational Engineering, Finance, and Science
Abstract

Computational modeling of Wnt signaling pathway has gained prominence for its use as computer aided diagnostic tool to develop therapeutic cancer target drugs and predict of test samples as cancerous and non cancerous. This manuscript focuses on development of simple static bayesian network models of varying complexity that encompasses prior partially available biological knowledge about intra and extra cellular factors affecting the Wnt pathway and incorporates epigenetic information like methylation and histone modification of a few genes known to have inhibitory affect on Wnt pathway. It might be expected that such models not only increase cancer prediction accuracies and also form basis for understanding Wnt signaling activity in different states of tumorigenesis. Initial results in human colorectal cancer cases indicate that incorporation of epigenetic information increases prediction accuracy of test samples as being tumorous or normal. Receiver Operator Curves (ROC) and their respective area under the curve (AUC) measurements, obtained from predictions of state of test sample and corresponding predictions of the state of activation of transcription complex of the Wnt pathway for the test sample, indicate that there is significant difference between the Wnt pathway being on (off) and its association with the sample being tumorous (normal). Two sample Kolmogorov-Smirnov test confirm the statistical deviation between the distributions of these predictions. At a preliminary stage, use of these models may help in understanding the yet unknown effect of certain factors like DKK2, DKK3-1 and SFRP-2/3/5 on {\beta}-catenin transcription complex., Comment: This paper has been withdrawn by the owner because it was submitted without consent of the co-authors

Published
2013

30. Personalised Travel Recommendation based on Location Co-occurrence

Author

Clements, Maarten, Serdyukov, Pavel, de Vries, Arjen P., and Reinders, Marcel J. T.

Subjects
Computer Science - Information Retrieval, H.3.5, H.3.3, H.2.8
Abstract

We propose a new task of recommending touristic locations based on a user's visiting history in a geographically remote region. This can be used to plan a touristic visit to a new city or country, or by travel agencies to provide personalised travel deals. A set of geotags is used to compute a location similarity model between two different regions. The similarity between two landmarks is derived from the number of users that have visited both places, using a Gaussian density estimation of the co-occurrence space of location visits to cluster related geotags. The standard deviation of the kernel can be used as a scale parameter that determines the size of the recommended landmarks. A personalised recommendation based on the location similarity model is evaluated on city and country scale and is able to outperform a location ranking based on popularity. Especially when a tourist filter based on visit duration is enforced, the prediction can be accurately adapted to the preference of the user. An extensive evaluation based on manual annotations shows that more strict ranking methods like cosine similarity and a proposed RankDiff algorithm provide more serendipitous recommendations and are able to link similar locations on opposite sides of the world., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Published
2011

32. Machine Learning Electronic Health Record Identification of Patients with Rheumatoid Arthritis: Algorithm Pipeline Development and Validation Study

Author

Maarseveen, Tjardo D, Meinderink, Timo, Reinders, Marcel J T, Knitza, Johannes, Huizinga, Tom W J, Kleyer, Arnd, Simon, David, van den Akker, Erik B, and Knevel, Rachel

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundFinancial codes are often used to extract diagnoses from electronic health records. This approach is prone to false positives. Alternatively, queries are constructed, but these are highly center and language specific. A tantalizing alternative is the automatic identification of patients by employing machine learning on format-free text entries. ObjectiveThe aim of this study was to develop an easily implementable workflow that builds a machine learning algorithm capable of accurately identifying patients with rheumatoid arthritis from format-free text fields in electronic health records. MethodsTwo electronic health record data sets were employed: Leiden (n=3000) and Erlangen (n=4771). Using a portion of the Leiden data (n=2000), we compared 6 different machine learning methods and a naïve word-matching algorithm using 10-fold cross-validation. Performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC), and F1 score was used as the primary criterion for selecting the best method to build a classifying algorithm. We selected the optimal threshold of positive predictive value for case identification based on the output of the best method in the training data. This validation workflow was subsequently applied to a portion of the Erlangen data (n=4293). For testing, the best performing methods were applied to remaining data (Leiden n=1000; Erlangen n=478) for an unbiased evaluation. ResultsFor the Leiden data set, the word-matching algorithm demonstrated mixed performance (AUROC 0.90; AUPRC 0.33; F1 score 0.55), and 4 methods significantly outperformed word-matching, with support vector machines performing best (AUROC 0.98; AUPRC 0.88; F1 score 0.83). Applying this support vector machine classifier to the test data resulted in a similarly high performance (F1 score 0.81; positive predictive value [PPV] 0.94), and with this method, we could identify 2873 patients with rheumatoid arthritis in less than 7 seconds out of the complete collection of 23,300 patients in the Leiden electronic health record system. For the Erlangen data set, gradient boosting performed best (AUROC 0.94; AUPRC 0.85; F1 score 0.82) in the training set, and applied to the test data, resulted once again in good results (F1 score 0.67; PPV 0.97). ConclusionsWe demonstrate that machine learning methods can extract the records of patients with rheumatoid arthritis from electronic health record data with high precision, allowing research on very large populations for limited costs. Our approach is language and center independent and could be applied to any type of diagnosis. We have developed our pipeline into a universally applicable and easy-to-implement workflow to equip centers with their own high-performing algorithm. This allows the creation of observational studies of unprecedented size covering different countries for low cost from already available data in electronic health record systems.

Published
2020
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35. Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk

Author

Kuiper, Lieke M, primary, Polinder-Bos, Harmke A, additional, Bizzarri, Daniele, additional, Vojinovic, Dina, additional, Vallerga, Costanza L, additional, Beekman, Marian, additional, Dollé, Martijn E T, additional, Ghanbari, Mohsen, additional, Voortman, Trudy, additional, Reinders, Marcel J T, additional, Verschuren, W M Monique, additional, Slagboom, P Eline, additional, van den Akker, Erik B, additional, and van Meurs, Joyce B J, additional

Published
2023
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37. The correlation between neuropathology levels and cognitive performance in centenarians

Author

Zhang, Meng, primary, Ganz, Andrea B., additional, Rohde, Susan, additional, Lorenz, Linda, additional, Rozemuller, Annemieke J. M., additional, van Vliet, Kimberley, additional, Graat, Marieke, additional, Sikkes, Sietske A. M., additional, Reinders, Marcel J. T., additional, Scheltens, Philip, additional, Hulsman, Marc, additional, Hoozemans, Jeroen J. M., additional, and Holstege, Henne, additional

Published
2023
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38. Machine learning-based somatic variant calling in cell-free DNA of metastatic breast cancer patients using large NGS panels

Author

Jongbloed, Elisabeth M., primary, Jansen, Maurice P.H.M., additional, de Weerd, Vanja, additional, Helmijr, Jean A., additional, Beaufort, Corine M., additional, Reinders, Marcel J. T., additional, Marion, Ronald van, additional, IJcken, Wilfred F. J. van, additional, Sonke, Gabe S., additional, Konings, Inge R. H. M., additional, Jager, Agnes, additional, Martens, John W.M., additional, Wilting, Saskia M., additional, and Makrodimitris, Stavros, additional

Published
2023
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41. A meta-analysis of genome-wide association studies identifies multiple longevity genes

Author

Deelen, Joris, Evans, Daniel S., Arking, Dan E., Tesi, Niccolò, Nygaard, Marianne, Liu, Xiaomin, Wojczynski, Mary K., Biggs, Mary L., van der Spek, Ashley, Atzmon, Gil, Ware, Erin B., Sarnowski, Chloé, Smith, Albert V., Seppälä, Ilkka, Cordell, Heather J., Dose, Janina, Amin, Najaf, Arnold, Alice M., Ayers, Kristin L., Barzilai, Nir, Becker, Elizabeth J., Beekman, Marian, Blanché, Hélène, Christensen, Kaare, Christiansen, Lene, Collerton, Joanna C., Cubaynes, Sarah, Cummings, Steven R., Davies, Karen, Debrabant, Birgit, Deleuze, Jean-François, Duncan, Rachel, Faul, Jessica D., Franceschi, Claudio, Galan, Pilar, Gudnason, Vilmundur, Harris, Tamara B., Huisman, Martijn, Hurme, Mikko A., Jagger, Carol, Jansen, Iris, Jylhä, Marja, Kähönen, Mika, Karasik, David, Kardia, Sharon L. R., Kingston, Andrew, Kirkwood, Thomas B. L., Launer, Lenore J., Lehtimäki, Terho, Lieb, Wolfgang, Lyytikäinen, Leo-Pekka, Martin-Ruiz, Carmen, Min, Junxia, Nebel, Almut, Newman, Anne B., Nie, Chao, Nohr, Ellen A., Orwoll, Eric S., Perls, Thomas T., Province, Michael A., Psaty, Bruce M., Raitakari, Olli T., Reinders, Marcel J. T., Robine, Jean-Marie, Rotter, Jerome I., Sebastiani, Paola, Smith, Jennifer, Sørensen, Thorkild I. A., Taylor, Kent D., Uitterlinden, André G., van der Flier, Wiesje, van der Lee, Sven J., van Duijn, Cornelia M., van Heemst, Diana, Vaupel, James W., Weir, David, Ye, Kenny, Zeng, Yi, Zheng, Wanlin, Holstege, Henne, Kiel, Douglas P., Lunetta, Kathryn L., Slagboom, P. Eline, and Murabito, Joanne M.

Published
2019
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42. Technical Report: A Comprehensive Comparison between Different Quantification Versions of Nightingale Health's 1 H-NMR Metabolomics Platform.

Author

Bizzarri, Daniele, Reinders, Marcel J. T., Beekman, Marian, Slagboom, P. Eline, and van den Akker, Erik B.

Subjects
TECHNICAL reports, METABOLOMICS, NIGHTINGALE, RANK correlation (Statistics), FATTY acids
Abstract

1H-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of 1H-NMR metabolomics, has recently updated the quantification strategy to further align with clinical standards. Such updates, however, might influence backward replicability, particularly affecting studies with repeated measures. Using data from BBMRI-NL consortium (~28,000 samples from 28 cohorts), we compared Nightingale data, originally released in 2014 and 2016, with a re-quantified version released in 2020, of which both versions were based on the same NMR spectra. Apart from two discontinued and twenty-three new analytes, we generally observe a high concordance between quantification versions with 73 out of 222 (33%) analytes showing a mean ρ > 0.9 across all cohorts. Conversely, five analytes consistently showed lower Spearman's correlations ( ρ < 0.7) between versions, namely acetoacetate, LDL-L, saturated fatty acids, S-HDL-C, and sphingomyelins. Furthermore, previously trained multi-analyte scores, such as MetaboAge or MetaboHealth, might be particularly sensitive to platform changes. Whereas MetaboHealth replicated well, the MetaboAge score had to be retrained due to use of discontinued analytes. Notably, both scores in the re-quantified data recapitulated mortality associations observed previously. Concluding, we urge caution in utilizing different platform versions to avoid mixing analytes, having different units, or simply being discontinued. [ABSTRACT FROM AUTHOR]

Published
2023
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50. Single-cell immune profiling reveals thymus-seeding populations, T cell commitment, and multilineage development in the human thymus

Author

Cordes, Martijn, primary, Canté-Barrett, Kirsten, additional, van den Akker, Erik B., additional, Moretti, Federico A., additional, Kiełbasa, Szymon M., additional, Vloemans, Sandra A., additional, Garcia-Perez, Laura, additional, Teodosio, Cristina, additional, van Dongen, Jacques J. M., additional, Pike-Overzet, Karin, additional, Reinders, Marcel J. T., additional, and Staal, Frank J. T., additional

Published
2022
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