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1. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

Zhou, Naihui, Jiang, Yuxiang, Bergquist, Timothy R, Lee, Alexandra J, Kacsoh, Balint Z, Crocker, Alex W, Lewis, Kimberley A, Georghiou, George, Nguyen, Huy N, Hamid, Md Nafiz, Davis, Larry, Dogan, Tunca, Atalay, Volkan, Rifaioglu, Ahmet S, Dalkıran, Alperen, Cetin Atalay, Rengul, Zhang, Chengxin, Hurto, Rebecca L, Freddolino, Peter L, Zhang, Yang, Bhat, Prajwal, Supek, Fran, Fernández, José M, Gemovic, Branislava, Perovic, Vladimir R, Davidović, Radoslav S, Sumonja, Neven, Veljkovic, Nevena, Asgari, Ehsaneddin, Mofrad, Mohammad RK, Profiti, Giuseppe, Savojardo, Castrense, Martelli, Pier Luigi, Casadio, Rita, Boecker, Florian, Schoof, Heiko, Kahanda, Indika, Thurlby, Natalie, McHardy, Alice C, Renaux, Alexandre, Saidi, Rabie, Gough, Julian, Freitas, Alex A, Antczak, Magdalena, Fabris, Fabio, Wass, Mark N, Hou, Jie, Cheng, Jianlin, Wang, Zheng, Romero, Alfonso E, Paccanaro, Alberto, Yang, Haixuan, Goldberg, Tatyana, Zhao, Chenguang, Holm, Liisa, Törönen, Petri, Medlar, Alan J, Zosa, Elaine, Borukhov, Itamar, Novikov, Ilya, Wilkins, Angela, Lichtarge, Olivier, Chi, Po-Han, Tseng, Wei-Cheng, Linial, Michal, Rose, Peter W, Dessimoz, Christophe, Vidulin, Vedrana, Dzeroski, Saso, Sillitoe, Ian, Das, Sayoni, Lees, Jonathan Gill, Jones, David T, Wan, Cen, Cozzetto, Domenico, Fa, Rui, Torres, Mateo, Warwick Vesztrocy, Alex, Rodriguez, Jose Manuel, Tress, Michael L, Frasca, Marco, Notaro, Marco, Grossi, Giuliano, Petrini, Alessandro, Re, Matteo, Valentini, Giorgio, Mesiti, Marco, Roche, Daniel B, Reeb, Jonas, Ritchie, David W, Aridhi, Sabeur, Alborzi, Seyed Ziaeddin, Devignes, Marie-Dominique, Koo, Da Chen Emily, Bonneau, Richard, Gligorijević, Vladimir, Barot, Meet, Fang, Hai, Toppo, Stefano, and Lavezzo, Enrico

Subjects
Human Genome, Networking and Information Technology R&D (NITRD), Genetics, Generic health relevance, Animals, Biofilms, Candida albicans, Drosophila melanogaster, Genome, Bacterial, Genome, Fungal, Humans, Locomotion, Memory, Long-Term, Molecular Sequence Annotation, Pseudomonas aeruginosa, Protein function prediction, Long-term memory, Biofilm, Critical assessment, Community challenge, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Published
2019

3. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Jiang, Yuxiang, Oron, Tal Ronnen, Clark, Wyatt T, Bankapur, Asma R, D'Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S, Kahanda, Indika, Verspoor, Karin M, Ben-Hur, Asa, Koo, Emily, Penfold-Brown, Duncan, Shasha, Dennis, Youngs, Noah, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed ME, Martelli, Pier Luigi, Profiti, Giuseppe, Casadio, Rita, Cao, Renzhi, Zhong, Zhaolong, Cheng, Jianlin, Altenhoff, Adrian, Skunca, Nives, Dessimoz, Christophe, Dogan, Tunca, Hakala, Kai, Kaewphan, Suwisa, Mehryary, Farrokh, Salakoski, Tapio, Ginter, Filip, Fang, Hai, Smithers, Ben, Oates, Matt, Gough, Julian, Törönen, Petri, Koskinen, Patrik, Holm, Liisa, Chen, Ching-Tai, Hsu, Wen-Lian, Bryson, Kevin, Cozzetto, Domenico, Minneci, Federico, Jones, David T, Chapman, Samuel, C., Dukka B K., Khan, Ishita K, Kihara, Daisuke, Ofer, Dan, Rappoport, Nadav, Stern, Amos, Cibrian-Uhalte, Elena, Denny, Paul, Foulger, Rebecca E, Hieta, Reija, Legge, Duncan, Lovering, Ruth C, Magrane, Michele, Melidoni, Anna N, Mutowo-Meullenet, Prudence, Pichler, Klemens, Shypitsyna, Aleksandra, Li, Biao, Zakeri, Pooya, ElShal, Sarah, Tranchevent, Léon-Charles, Das, Sayoni, Dawson, Natalie L, Lee, David, Lees, Jonathan G, Sillitoe, Ian, Bhat, Prajwal, Nepusz, Tamás, Romero, Alfonso E, Sasidharan, Rajkumar, Yang, Haixuan, Paccanaro, Alberto, Gillis, Jesse, Sedeño-Cortés, Adriana E, Pavlidis, Paul, Feng, Shou, Cejuela, Juan M, Goldberg, Tatyana, Hamp, Tobias, Richter, Lothar, Salamov, Asaf, Gabaldon, Toni, Marcet-Houben, Marina, Supek, Fran, Gong, Qingtian, Ning, Wei, Zhou, Yuanpeng, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Toppo, Stefano, Ferrari, Carlo, Giollo, Manuel, Piovesan, Damiano, Tosatto, Silvio, del Pozo, Angela, Fernández, José M, Maietta, Paolo, Valencia, Alfonso, Tress, Michael L, Benso, Alfredo, Di Carlo, Stefano, Politano, Gianfranco, Savino, Alessandro, Rehman, Hafeez Ur, Re, Matteo, Mesiti, Marco, Valentini, Giorgio, Bargsten, Joachim W, van Dijk, Aalt DJ, Gemovic, Branislava, Glisic, Sanja, Perovic, Vladmir, Veljkovic, Veljko, Veljkovic, Nevena, Almeida-e-Silva, Danillo C, Vencio, Ricardo ZN, Sharan, Malvika, Vogel, Jörg, Kansakar, Lakesh, Zhang, Shanshan, Vucetic, Slobodan, Wang, Zheng, Sternberg, Michael JE, Wass, Mark N, Huntley, Rachael P, Martin, Maria J, O'Donovan, Claire, Robinson, Peter N, Moreau, Yves, Tramontano, Anna, Babbitt, Patricia C, Brenner, Steven E, Linial, Michal, Orengo, Christine A, Rost, Burkhard, Greene, Casey S, Mooney, Sean D, Friedberg, Iddo, and Radivojac, Predrag

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Background: The increasing volume and variety of genotypic and phenotypic data is a major defining characteristic of modern biomedical sciences. At the same time, the limitations in technology for generating data and the inherently stochastic nature of biomolecular events have led to the discrepancy between the volume of data and the amount of knowledge gleaned from it. A major bottleneck in our ability to understand the molecular underpinnings of life is the assignment of function to biological macromolecules, especially proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, accurately assessing methods for protein function prediction and tracking progress in the field remain challenging. Methodology: We have conducted the second Critical Assessment of Functional Annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. One hundred twenty-six methods from 56 research groups were evaluated for their ability to predict biological functions using the Gene Ontology and gene-disease associations using the Human Phenotype Ontology on a set of 3,681 proteins from 18 species. CAFA2 featured significantly expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2. Conclusions: The top performing methods in CAFA2 outperformed the best methods from CAFA1, demonstrating that computational function prediction is improving. This increased accuracy can be attributed to the combined effect of the growing number of experimental annotations and improved methods for function prediction., Comment: Submitted to Genome Biology

Published
2016
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4. Scientific impact evaluation and the effect of self-citations: mitigating the bias by discounting h-index

Author

Ferrara, Emilio and Romero, Alfonso E.

Subjects
Computer Science - Digital Libraries
Abstract

In this paper, we propose a measure to assess scientific impact that discounts self-citations and does not require any prior knowledge on the their distribution among publications. This index can be applied to both researchers and journals. In particular, we show that it fills the gap of h-index and similar measures that do not take into account the effect of self-citations for authors or journals impact evaluation. The paper provides with two real-world examples: in the former, we evaluate the research impact of the most productive scholars in Computer Science (according to DBLP); in the latter, we revisit the impact of the journals ranked in the 'Computer Science Applications' section of SCImago. We observe how self-citations, in many cases, affect the rankings obtained according to different measures (including h-index and ch-index), and show how the proposed measure mitigates this effect.

Published
2012
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5. A probabilistic methodology for multilabel classification

Author

Romero, Alfonso E. and de Campos, Luis M.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Learning, 68T37, 68T10, I.2.6, I.2.3, H.3
Abstract

Multilabel classification is a relatively recent subfield of machine learning. Unlike to the classical approach, where instances are labeled with only one category, in multilabel classification, an arbitrary number of categories is chosen to label an instance. Due to the problem complexity (the solution is one among an exponential number of alternatives), a very common solution (the binary method) is frequently used, learning a binary classifier for every category, and combining them all afterwards. The assumption taken in this solution is not realistic, and in this work we give examples where the decisions for all the labels are not taken independently, and thus, a supervised approach should learn those existing relationships among categories to make a better classification. Therefore, we show here a generic methodology that can improve the results obtained by a set of independent probabilistic binary classifiers, by using a combination procedure with a classifier trained on the co-occurrences of the labels. We show an exhaustive experimentation in three different standard corpora of labeled documents (Reuters-21578, Ohsumed-23 and RCV1), which present noticeable improvements in all of them, when using our methodology, in three probabilistic base classifiers., Comment: 14 pages, 1 figure, under review

Published
2012

6. A large-scale evaluation of computational protein function prediction

Author

Radivojac, Predrag, Clark, Wyatt T, Oron, Tal Ronnen, Schnoes, Alexandra M, Wittkop, Tobias, Sokolov, Artem, Graim, Kiley, Funk, Christopher, Verspoor, Karin, Ben-Hur, Asa, Pandey, Gaurav, Yunes, Jeffrey M, Talwalkar, Ameet S, Repo, Susanna, Souza, Michael L, Piovesan, Damiano, Casadio, Rita, Wang, Zheng, Cheng, Jianlin, Fang, Hai, Gough, Julian, Koskinen, Patrik, Törönen, Petri, Nokso-Koivisto, Jussi, Holm, Liisa, Cozzetto, Domenico, Buchan, Daniel WA, Bryson, Kevin, Jones, David T, Limaye, Bhakti, Inamdar, Harshal, Datta, Avik, Manjari, Sunitha K, Joshi, Rajendra, Chitale, Meghana, Kihara, Daisuke, Lisewski, Andreas M, Erdin, Serkan, Venner, Eric, Lichtarge, Olivier, Rentzsch, Robert, Yang, Haixuan, Romero, Alfonso E, Bhat, Prajwal, Paccanaro, Alberto, Hamp, Tobias, Kaßner, Rebecca, Seemayer, Stefan, Vicedo, Esmeralda, Schaefer, Christian, Achten, Dominik, Auer, Florian, Boehm, Ariane, Braun, Tatjana, Hecht, Maximilian, Heron, Mark, Hönigschmid, Peter, Hopf, Thomas A, Kaufmann, Stefanie, Kiening, Michael, Krompass, Denis, Landerer, Cedric, Mahlich, Yannick, Roos, Manfred, Björne, Jari, Salakoski, Tapio, Wong, Andrew, Shatkay, Hagit, Gatzmann, Fanny, Sommer, Ingolf, Wass, Mark N, Sternberg, Michael JE, Škunca, Nives, Supek, Fran, Bošnjak, Matko, Panov, Panče, Džeroski, Sašo, Šmuc, Tomislav, Kourmpetis, Yiannis AI, van Dijk, Aalt DJ, Braak, Cajo JF ter, Zhou, Yuanpeng, Gong, Qingtian, Dong, Xinran, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Di Camillo, Barbara, Toppo, Stefano, Lan, Liang, Djuric, Nemanja, Guo, Yuhong, Vucetic, Slobodan, Bairoch, Amos, Linial, Michal, Babbitt, Patricia C, Brenner, Steven E, Orengo, Christine, and Rost, Burkhard

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Algorithms, Animals, Computational Biology, Databases, Protein, Exoribonucleases, Forecasting, Humans, Molecular Biology, Molecular Sequence Annotation, Proteins, Species Specificity, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Published
2013

7. Activity Recognition for an Agent-Oriented Personal Health System

Author

Kafalı, Özgür, Romero, Alfonso E., Stathis, Kostas, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Dam, Hoa Khanh, editor, Pitt, Jeremy, editor, Xu, Yang, editor, Governatori, Guido, editor, and Ito, Takayuki, editor

Published
2014
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8. Link-Based Text Classification Using Bayesian Networks

Author

de Campos, Luis M., Fernández-Luna, Juan M., Huete, Juan F., Masegosa, Andrés R., Romero, Alfonso E., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Geva, Shlomo, editor, Kamps, Jaap, editor, and Trotman, Andrew, editor

Published
2010
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10. Probabilistic Methods for Link-Based Classification at INEX 2008

Author

de Campos, Luis M., Fernández-Luna, Juan M., Huete, Juan F., Romero, Alfonso E., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Geva, Shlomo, editor, Kamps, Jaap, editor, and Trotman, Andrew, editor

Published
2009
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11. New Utility Models for the Garnata Information Retrieval System at INEX’08

Author

de Campos, Luis M., Fernández-Luna, Juan M., Huete, Juan F., Martín-Dancausa, Carlos, Romero, Alfonso E., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Geva, Shlomo, editor, Kamps, Jaap, editor, and Trotman, Andrew, editor

Published
2009
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12. Probabilistic Methods for Structured Document Classification at INEX’07

Author

de Campos, Luis M., Fernández-Luna, Juan M., Huete, Juan F., Romero, Alfonso E., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Fuhr, Norbert, editor, Kamps, Jaap, editor, Lalmas, Mounia, editor, and Trotman, Andrew, editor

Published
2008
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13. The Garnata Information Retrieval System at INEX’07

Author

de Campos, Luis M., Fernández-Luna, Juan M., Huete, Juan F., Martín-Dancausa, Carlos, Romero, Alfonso E., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Fuhr, Norbert, editor, Kamps, Jaap, editor, Lalmas, Mounia, editor, and Trotman, Andrew, editor

Published
2008
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14. Influence Diagrams and Structured Retrieval: Garnata Implementing the SID and CID Models at INEX’06

Author

de Campos, Luis M., Fernández-Luna, Juan M., Huete, Juan F., Romero, Alfonso E., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Fuhr, Norbert, editor, Lalmas, Mounia, editor, and Trotman, Andrew, editor

Published
2007
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26. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

Zhou, Naihui, primary, Jiang, Yuxiang, additional, Bergquist, Timothy R, additional, Lee, Alexandra J, additional, Kacsoh, Balint Z, additional, Crocker, Alex W, additional, Lewis, Kimberley A, additional, Georghiou, George, additional, Nguyen, Huy N, additional, Hamid, Md Nafiz, additional, Davis, Larry, additional, Dogan, Tunca, additional, Atalay, Volkan, additional, Rifaioglu, Ahmet S, additional, Dalkiran, Alperen, additional, Cetin-Atalay, Rengul, additional, Zhang, Chengxin, additional, Hurto, Rebecca L, additional, Freddolino, Peter L, additional, Zhang, Yang, additional, Bhat, Prajwal, additional, Supek, Fran, additional, Fernández, José M, additional, Gemovic, Branislava, additional, Perovic, Vladimir R, additional, Davidović, Radoslav S, additional, Sumonja, Neven, additional, Veljkovic, Nevena, additional, Asgari, Ehsaneddin, additional, Mofrad, Mohammad RK, additional, Profiti, Giuseppe, additional, Savojardo, Castrense, additional, Martelli, Pier Luigi, additional, Casadio, Rita, additional, Boecker, Florian, additional, Kahanda, Indika, additional, Thurlby, Natalie, additional, McHardy, Alice C, additional, Renaux, Alexandre, additional, Saidi, Rabie, additional, Gough, Julian, additional, Freitas, Alex A, additional, Antczak, Magdalena, additional, Fabris, Fabio, additional, Wass, Mark N, additional, Hou, Jie, additional, Cheng, Jianlin, additional, Wang, Zheng, additional, Romero, Alfonso E, additional, Paccanaro, Alberto, additional, Yang, Haixuan, additional, Goldberg, Tatyana, additional, Zhao, Chenguang, additional, Holm, Liisa, additional, Törönen, Petri, additional, Medlar, Alan J, additional, Zosa, Elaine, additional, Borukhov, Itamar, additional, Novikov, Ilya, additional, Wilkins, Angela, additional, Lichtarge, Olivier, additional, Chi, Po-Han, additional, Tseng, Wei-Cheng, additional, Linial, Michal, additional, Rose, Peter W, additional, Dessimoz, Christophe, additional, Vidulin, Vedrana, additional, Dzeroski, Saso, additional, Sillitoe, Ian, additional, Das, Sayoni, additional, Lees, Jonathan Gill, additional, Jones, David T, additional, Wan, Cen, additional, Cozzetto, Domenico, additional, Fa, Rui, additional, Torres, Mateo, additional, Vesztrocy, Alex Wiarwick, additional, Rodriguez, Jose Manuel, additional, Tress, Michael L, additional, Frasca, Marco, additional, Notaro, Marco, additional, Grossi, Giuliano, additional, Petrini, Alessandro, additional, Re, Matteo, additional, Valentini, Giorgio, additional, Mesiti, Marco, additional, Roche, Daniel B, additional, Reeb, Jonas, additional, Ritchie, David W, additional, Aridhi, Sabeur, additional, Alborzi, Seyed Ziaeddin, additional, Devignes, Marie-Dominique, additional, Emily Koo, Da Chen, additional, Bonneau, Richard, additional, Gligorijević, Vladimir, additional, Barot, Meet, additional, Fang, Hai, additional, Toppo, Stefano, additional, Lavezzo, Enrico, additional, Falda, Marco, additional, Berselli, Michele, additional, Tosatto, Silvio CE, additional, Carraro, Marco, additional, Piovesan, Damiano, additional, Rehman, Hafeez Ur, additional, Mao, Qizhong, additional, Zhang, Shanshan, additional, Vucetic, Slobodan, additional, Black, Gage S, additional, Jo, Dane, additional, Larsen, Dallas J, additional, Omdahl, Ashton R, additional, Sagers, Luke W, additional, Suh, Erica, additional, Dayton, Jonathan B, additional, McGuffin, Liam J, additional, Brackenridge, Danielle A, additional, Babbitt, Patricia C, additional, Yunes, Jeffrey M, additional, Fontana, Paolo, additional, Zhang, Feng, additional, Zhu, Shanfeng, additional, You, Ronghui, additional, Zhang, Zihan, additional, Dai, Suyang, additional, Yao, Shuwei, additional, Tian, Weidong, additional, Cao, Renzhi, additional, Chandler, Caleb, additional, Amezola, Miguel, additional, Johnson, Devon, additional, Chang, Jia-Ming, additional, Liao, Wen-Hung, additional, Liu, Yi-Wei, additional, Pascarelli, Stefano, additional, Frank, Yotam, additional, Hoehndorf, Robert, additional, Kulmanov, Maxat, additional, Boudellioua, Imane, additional, Politano, Gianfranco, additional, Di Carlo, Stefano, additional, Benso, Alfredo, additional, Hakala, Kai, additional, Ginter, Filip, additional, Mehryary, Farrokh, additional, Kaewphan, Suwisa, additional, Björne, Jari, additional, Moen, Hans, additional, Tolvanen, Martti E E, additional, Salakoski, Tapio, additional, Kihara, Daisuke, additional, Jain, Aashish, additional, Šmuc, Tomislav, additional, Altenhoff, Adrian, additional, Ben-Hur, Asa, additional, Rost, Burkhard, additional, Brenner, Steven E, additional, Orengo, Christine A, additional, Jeffery, Constance J, additional, Bosco, Giovanni, additional, Hogan, Deborah A, additional, Martin, Maria J, additional, O’Donovan, Claire, additional, Mooney, Sean D, additional, Greene, Casey S, additional, Radivojac, Predrag, additional, and Friedberg, Iddo, additional

Published
2019
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27. Additional file 1 of An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Yuxiang Jiang, Oron, Tal Ronnen, Clark, Wyatt T., Bankapur, Asma R., D’Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S., Indika Kahanda, Verspoor, Karin M., Ben-Hur, Asa, Koo, Da Chen Emily, Penfold-Brown, Duncan, Shasha, Dennis, Youngs, Noah, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed M. E., Martelli, Pier Luigi, Profiti, Giuseppe, Casadio, Rita, Renzhi Cao, Zhaolong Zhong, Jianlin Cheng, Altenhoff, Adrian, Skunca, Nives, Dessimoz, Christophe, Tunca Dogan, Hakala, Kai, Suwisa Kaewphan, Mehryary, Farrokh, Salakoski, Tapio, Ginter, Filip, Fang, Hai, Smithers, Ben, Oates, Matt, Gough, Julian, Törönen, Petri, Koskinen, Patrik, Holm, Liisa, Ching-Tai Chen, Hsu, Wen-Lian, Bryson, Kevin, Cozzetto, Domenico, Minneci, Federico, Jones, David T., Chapman, Samuel, Dukka BKC, Ishita K. Khan, Kihara, Daisuke, Ofer, Dan, Rappoport, Nadav, Stern, Amos, Cibrian-Uhalte, Elena, Denny, Paul, Foulger, Rebecca E., Hieta, Reija, Legge, Duncan, Lovering, Ruth C., Magrane, Michele, Melidoni, Anna N., Mutowo-Meullenet, Prudence, Pichler, Klemens, Shypitsyna, Aleksandra, Li, Biao, Pooya Zakeri, ElShal, Sarah, Léon-Charles Tranchevent, Sayoni Das, Dawson, Natalie L., Lee, David, Lees, Jonathan G., Sillitoe, Ian, Prajwal Bhat, Nepusz, Tamás, Romero, Alfonso E., Sasidharan, Rajkumar, Haixuan Yang, Paccanaro, Alberto, Gillis, Jesse, Sedeño-Cortés, Adriana E., Pavlidis, Paul, Feng, Shou, Cejuela, Juan M., Goldberg, Tatyana, Hamp, Tobias, Richter, Lothar, Salamov, Asaf, Gabaldon, Toni, Marcet-Houben, Marina, Supek, Fran, Qingtian Gong, Ning, Wei, Yuanpeng Zhou, Weidong Tian, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Toppo, Stefano, Ferrari, Carlo, Giollo, Manuel, Piovesan, Damiano, Tosatto, Silvio C.E., Pozo, Angela Del, Fernández, José M., Maietta, Paolo, Valencia, Alfonso, Tress, Michael L., Benso, Alfredo, Carlo, Stefano Di, Politano, Gianfranco, Savino, Alessandro, Hafeez Ur Rehman, Re, Matteo, Mesiti, Marco, Valentini, Giorgio, Bargsten, Joachim W., Dijk, Aalt D. J. Van, Gemovic, Branislava, Glisic, Sanja, Vladmir Perovic, Veljkovic, Veljko, Veljkovic, Nevena, Danillo C. Almeida-E-Silva, Vencio, Ricardo Z. N., Malvika Sharan, Vogel, Jörg, Lakesh Kansakar, Shanshan Zhang, Vucetic, Slobodan, Wang, Zheng, Sternberg, Michael J. E., Wass, Mark N., Huntley, Rachael P., Martin, Maria J., O’Donovan, Claire, Robinson, Peter N., Moreau, Yves, Tramontano, Anna, Babbitt, Patricia C., Brenner, Steven E., Linial, Michal, Orengo, Christine A., Rost, Burkhard, Greene, Casey S., Mooney, Sean D., Friedberg, Iddo, and Radivojac, Predrag

Abstract

A document containing a subset of CAFA2 analyses that are equivalent to those provided about the CAFA1 experiment in the CAFA1 supplement. (PDF 11100 kb)

Published
2016
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30. Agent-oriented activity recognition in the event calculus: An application for diabetic patients

Author

Kafalı, Özgur, Romero, Alfonso E., Stathis, Kostas, Kafalı, Özgur, Romero, Alfonso E., and Stathis, Kostas

Abstract

We present a knowledge representation framework on the basis of the Event Calculus that allows an agent to recognize complex activities from low-level observations received by multiple sensors, reason about the life cycle of such activities, and take action to support their successful completion. Activities are multivalue fluents that change according to events that occur in the environment. The parameters of an activity consist of a unique label, a set of participants involved in the performing of the activity, and a unique goal associated with the activity revealing the activity's desired outcome. Our contribution is the identification of an activity life cycle describing how activities can be started, interrupted, suspended, resumed, or completed over time, as well as how these can be represented. The framework also specifies activity goals, their associated life cycle, and their relation with the activity life cycle. We provide the complete implementation of the framework, which includes an activity generator that automatically creates synthetic sensor data in the form of event streams that represent the everyday lifestyle of a type 1 diabetic patient. Moreover, we test the framework by generating very large activity streams that we use to evaluate the performance of the recognition capability and study its relative merits.

Published
2017

31. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Jiang, Yuxiang, primary, Oron, Tal Ronnen, additional, Clark, Wyatt T., additional, Bankapur, Asma R., additional, D’Andrea, Daniel, additional, Lepore, Rosalba, additional, Funk, Christopher S., additional, Kahanda, Indika, additional, Verspoor, Karin M., additional, Ben-Hur, Asa, additional, Koo, Da Chen Emily, additional, Penfold-Brown, Duncan, additional, Shasha, Dennis, additional, Youngs, Noah, additional, Bonneau, Richard, additional, Lin, Alexandra, additional, Sahraeian, Sayed M. E., additional, Martelli, Pier Luigi, additional, Profiti, Giuseppe, additional, Casadio, Rita, additional, Cao, Renzhi, additional, Zhong, Zhaolong, additional, Cheng, Jianlin, additional, Altenhoff, Adrian, additional, Skunca, Nives, additional, Dessimoz, Christophe, additional, Dogan, Tunca, additional, Hakala, Kai, additional, Kaewphan, Suwisa, additional, Mehryary, Farrokh, additional, Salakoski, Tapio, additional, Ginter, Filip, additional, Fang, Hai, additional, Smithers, Ben, additional, Oates, Matt, additional, Gough, Julian, additional, Törönen, Petri, additional, Koskinen, Patrik, additional, Holm, Liisa, additional, Chen, Ching-Tai, additional, Hsu, Wen-Lian, additional, Bryson, Kevin, additional, Cozzetto, Domenico, additional, Minneci, Federico, additional, Jones, David T., additional, Chapman, Samuel, additional, BKC, Dukka, additional, Khan, Ishita K., additional, Kihara, Daisuke, additional, Ofer, Dan, additional, Rappoport, Nadav, additional, Stern, Amos, additional, Cibrian-Uhalte, Elena, additional, Denny, Paul, additional, Foulger, Rebecca E., additional, Hieta, Reija, additional, Legge, Duncan, additional, Lovering, Ruth C., additional, Magrane, Michele, additional, Melidoni, Anna N., additional, Mutowo-Meullenet, Prudence, additional, Pichler, Klemens, additional, Shypitsyna, Aleksandra, additional, Li, Biao, additional, Zakeri, Pooya, additional, ElShal, Sarah, additional, Tranchevent, Léon-Charles, additional, Das, Sayoni, additional, Dawson, Natalie L., additional, Lee, David, additional, Lees, Jonathan G., additional, Sillitoe, Ian, additional, Bhat, Prajwal, additional, Nepusz, Tamás, additional, Romero, Alfonso E., additional, Sasidharan, Rajkumar, additional, Yang, Haixuan, additional, Paccanaro, Alberto, additional, Gillis, Jesse, additional, Sedeño-Cortés, Adriana E., additional, Pavlidis, Paul, additional, Feng, Shou, additional, Cejuela, Juan M., additional, Goldberg, Tatyana, additional, Hamp, Tobias, additional, Richter, Lothar, additional, Salamov, Asaf, additional, Gabaldon, Toni, additional, Marcet-Houben, Marina, additional, Supek, Fran, additional, Gong, Qingtian, additional, Ning, Wei, additional, Zhou, Yuanpeng, additional, Tian, Weidong, additional, Falda, Marco, additional, Fontana, Paolo, additional, Lavezzo, Enrico, additional, Toppo, Stefano, additional, Ferrari, Carlo, additional, Giollo, Manuel, additional, Piovesan, Damiano, additional, Tosatto, Silvio C.E., additional, del Pozo, Angela, additional, Fernández, José M., additional, Maietta, Paolo, additional, Valencia, Alfonso, additional, Tress, Michael L., additional, Benso, Alfredo, additional, Di Carlo, Stefano, additional, Politano, Gianfranco, additional, Savino, Alessandro, additional, Rehman, Hafeez Ur, additional, Re, Matteo, additional, Mesiti, Marco, additional, Valentini, Giorgio, additional, Bargsten, Joachim W., additional, van Dijk, Aalt D. J., additional, Gemovic, Branislava, additional, Glisic, Sanja, additional, Perovic, Vladmir, additional, Veljkovic, Veljko, additional, Veljkovic, Nevena, additional, Almeida-e-Silva, Danillo C., additional, Vencio, Ricardo Z. N., additional, Sharan, Malvika, additional, Vogel, Jörg, additional, Kansakar, Lakesh, additional, Zhang, Shanshan, additional, Vucetic, Slobodan, additional, Wang, Zheng, additional, Sternberg, Michael J. E., additional, Wass, Mark N., additional, Huntley, Rachael P., additional, Martin, Maria J., additional, O’Donovan, Claire, additional, Robinson, Peter N., additional, Moreau, Yves, additional, Tramontano, Anna, additional, Babbitt, Patricia C., additional, Brenner, Steven E., additional, Linial, Michal, additional, Orengo, Christine A., additional, Rost, Burkhard, additional, Greene, Casey S., additional, Mooney, Sean D., additional, Friedberg, Iddo, additional, and Radivojac, Predrag, additional

Published
2016
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33. Neurogenomic Signatures of Successes and Failures in Life-History Transitions in a Key Insect Pollinator.

Author

Brown, Mark J. F., Manfredini, Fabio, Romero, Alfonso E., Paccanaro, Alberto, Pedroso, Inti, and Sumner, Seirian

Subjects
BUMBLEBEES, BOMBUS terrestris, DEVELOPMENTAL biology, BRAIN, GENE regulatory networks
Abstract

Life-history transitions require major reprogramming at the behavioral and physiological level. Mating and reproductive maturation are known to trigger changes in gene transcription in reproductive tissues in a wide range of organisms, but we understand little about the molecular consequences of a failure to mate or become reproductively mature, and it is not clear to what extent these processes trigger neural as well as physiological changes. In this study, we examined the molecular processes underpinning the behavioral changes that accompany the major life-history transitions in a key pollinator, the bumblebee Bombus terrestris. We compared neuro-transcription in queens that succeeded or failed in switching from virgin and immature states, to mated and reproductively mature states. Both successes and failures were associated with distinct molecular profiles, illustrating how development during adulthood triggers distinct molecular profiles within a single caste of a eusocial insect. Failures in both mating and reproductive maturation were explained by a general up-regulation of brain gene transcription. We identified 21 genes that were highly connected in a gene coexpression network analysis: nine genes are involved in neural processes and four are regulators of gene expression. This suggests that negotiating life-history transitions involves significant neural processing and reprogramming, and not just changes in physiology. These findings provide novel insights into basic life-history transitions of an insect. Failure to mate or to become reproductively mature is an overlooked component of variation in natural systems, despite its prevalence in many sexually reproducing organisms, and deserves deeper investigation in the future. [ABSTRACT FROM AUTHOR]

Published
2017
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39. Automatic Indexing from a Thesaurus Using Bayesian Networks: Application to the Classification of Parliamentary Initiatives.

Author

Carbonell, Jaime G., Siekmann, Jörg, Mellouli, Khaled, de Campos, Luis M., Fernández-Luna, Juan M., Huete, Juan F., and Romero, Alfonso E.

Abstract

We propose a method which, given a document to be classified, automatically generates an ordered set of appropriate descriptors extracted from a thesaurus. The method creates a Bayesian network to model the thesaurus and uses probabilistic inference to select the set of descriptors having high posterior probability of being relevant given the available evidence (the document to be classified). We apply the method to the classification of parliamentary initiatives in the regional Parliament of Andalucía at Spain from the Eurovoc thesaurus. [ABSTRACT FROM AUTHOR]

Published
2007
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44. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Yuxiang Jiang, Tal Ronnen Oron, Wyatt T. Clark, Asma R. Bankapur, Daniel D’Andrea, Rosalba Lepore, Christopher S. Funk, Indika Kahanda, Karin M. Verspoor, Asa Ben-Hur, Da Chen Emily Koo, Duncan Penfold-Brown, Dennis Shasha, Noah Youngs, Richard Bonneau, Alexandra Lin, Sayed M. E. Sahraeian, Pier Luigi Martelli, Giuseppe Profiti, Rita Casadio, Renzhi Cao, Zhaolong Zhong, Jianlin Cheng, Adrian Altenhoff, Nives Skunca, Christophe Dessimoz, Tunca Dogan, Kai Hakala, Suwisa Kaewphan, Farrokh Mehryary, Tapio Salakoski, Filip Ginter, Hai Fang, Ben Smithers, Matt Oates, Julian Gough, Petri Törönen, Patrik Koskinen, Liisa Holm, Ching-Tai Chen, Wen-Lian Hsu, Kevin Bryson, Domenico Cozzetto, Federico Minneci, David T. Jones, Samuel Chapman, Dukka BKC, Ishita K. Khan, Daisuke Kihara, Dan Ofer, Nadav Rappoport, Amos Stern, Elena Cibrian-Uhalte, Paul Denny, Rebecca E. Foulger, Reija Hieta, Duncan Legge, Ruth C. Lovering, Michele Magrane, Anna N. Melidoni, Prudence Mutowo-Meullenet, Klemens Pichler, Aleksandra Shypitsyna, Biao Li, Pooya Zakeri, Sarah ElShal, Léon-Charles Tranchevent, Sayoni Das, Natalie L. Dawson, David Lee, Jonathan G. Lees, Ian Sillitoe, Prajwal Bhat, Tamás Nepusz, Alfonso E. Romero, Rajkumar Sasidharan, Haixuan Yang, Alberto Paccanaro, Jesse Gillis, Adriana E. Sedeño-Cortés, Paul Pavlidis, Shou Feng, Juan M. Cejuela, Tatyana Goldberg, Tobias Hamp, Lothar Richter, Asaf Salamov, Toni Gabaldon, Marina Marcet-Houben, Fran Supek, Qingtian Gong, Wei Ning, Yuanpeng Zhou, Weidong Tian, Marco Falda, Paolo Fontana, Enrico Lavezzo, Stefano Toppo, Carlo Ferrari, Manuel Giollo, Damiano Piovesan, Silvio C.E. Tosatto, Angela del Pozo, José M. Fernández, Paolo Maietta, Alfonso Valencia, Michael L. Tress, Alfredo Benso, Stefano Di Carlo, Gianfranco Politano, Alessandro Savino, Hafeez Ur Rehman, Matteo Re, Marco Mesiti, Giorgio Valentini, Joachim W. Bargsten, Aalt D. J. van Dijk, Branislava Gemovic, Sanja Glisic, Vladmir Perovic, Veljko Veljkovic, Nevena Veljkovic, Danillo C. Almeida-e-Silva, Ricardo Z. N. Vencio, Malvika Sharan, Jörg Vogel, Lakesh Kansakar, Shanshan Zhang, Slobodan Vucetic, Zheng Wang, Michael J. E. Sternberg, Mark N. Wass, Rachael P. Huntley, Maria J. Martin, Claire O’Donovan, Peter N. Robinson, Yves Moreau, Anna Tramontano, Patricia C. Babbitt, Steven E. Brenner, Michal Linial, Christine A. Orengo, Burkhard Rost, Casey S. Greene, Sean D. Mooney, Iddo Friedberg, Predrag Radivojac, Jiang, Yuxiang, Oron, Tal Ronnen, Clark, Wyatt T., Bankapur, Asma R., D’Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S., Kahanda, Indika, Verspoor, Karin M., Ben-Hur, Asa, Koo, Da Chen Emily, Penfold-Brown, Duncan, Shasha, Denni, Youngs, Noah, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed M. E., Martelli, Pier Luigi, Profiti, Giuseppe, Casadio, Rita, Cao, Renzhi, Zhong, Zhaolong, Cheng, Jianlin, Altenhoff, Adrian, Skunca, Nive, Dessimoz, Christophe, Dogan, Tunca, Hakala, Kai, Kaewphan, Suwisa, Mehryary, Farrokh, Salakoski, Tapio, Ginter, Filip, Fang, Hai, Smithers, Ben, Oates, Matt, Gough, Julian, Törönen, Petri, Koskinen, Patrik, Holm, Liisa, Chen, Ching-Tai, Hsu, Wen-Lian, Bryson, Kevin, Cozzetto, Domenico, Minneci, Federico, Jones, David T., Chapman, Samuel, Bkc, Dukka, Khan, Ishita K., Kihara, Daisuke, Ofer, Dan, Rappoport, Nadav, Stern, Amo, Cibrian-Uhalte, Elena, Denny, Paul, Foulger, Rebecca E., Hieta, Reija, Legge, Duncan, Lovering, Ruth C., Magrane, Michele, Melidoni, Anna N., Mutowo-Meullenet, Prudence, Pichler, Klemen, Shypitsyna, Aleksandra, Li, Biao, Zakeri, Pooya, Elshal, Sarah, Tranchevent, Léon-Charle, Das, Sayoni, Dawson, Natalie L., Lee, David, Lees, Jonathan G., Sillitoe, Ian, Bhat, Prajwal, Nepusz, Tamá, Romero, Alfonso E., Sasidharan, Rajkumar, Yang, Haixuan, Paccanaro, Alberto, Gillis, Jesse, Sedeño-Cortés, Adriana E., Pavlidis, Paul, Feng, Shou, Cejuela, Juan M., Goldberg, Tatyana, Hamp, Tobia, Richter, Lothar, Salamov, Asaf, Gabaldon, Toni, Marcet-Houben, Marina, Supek, Fran, Gong, Qingtian, Ning, Wei, Zhou, Yuanpeng, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Toppo, Stefano, Ferrari, Carlo, Giollo, Manuel, Piovesan, Damiano, Tosatto, Silvio C.E., del Pozo, Angela, Fernández, José M., Maietta, Paolo, Valencia, Alfonso, Tress, Michael L., Benso, Alfredo, Di Carlo, Stefano, Politano, Gianfranco, Savino, Alessandro, Rehman, Hafeez Ur, Re, Matteo, Mesiti, Marco, Valentini, Giorgio, Bargsten, Joachim W., van Dijk, Aalt D. J., Gemovic, Branislava, Glisic, Sanja, Perovic, Vladmir, Veljkovic, Veljko, Veljkovic, Nevena, Almeida-e-Silva, Danillo C., Vencio, Ricardo Z. N., Sharan, Malvika, Vogel, Jörg, Kansakar, Lakesh, Zhang, Shanshan, Vucetic, Slobodan, Wang, Zheng, Sternberg, Michael J. E., Wass, Mark N., Huntley, Rachael P., Martin, Maria J., O’Donovan, Claire, Robinson, Peter N., Moreau, Yve, Tramontano, Anna, Babbitt, Patricia C., Brenner, Steven E., Linial, Michal, Orengo, Christine A., Rost, Burkhard, Greene, Casey S., Mooney, Sean D., Friedberg, Iddo, Radivojac, Predrag, Friedberg, Iddo [0000-0002-1789-8000], Apollo - University of Cambridge Repository, (ukupan broj autora: 147), Biotechnology and Biological Sciences Research Council (BBSRC), National Science Foundation (Estados Unidos), United States of Department of Health & Human Services, National Natural Science Foundation of China, Natural Sciences and Engineering Research Council (Canadá), São Paulo Research Foundation, Ministerio de Economía y Competitividad (España), Biotechnology and Biological Sciences Research Council (Reino Unido), Katholieke Universiteit Leuven (Bélgica), Newton International Fellowship Scheme of the Royal Society grant, British Heart Foundation, Ministry of Education, Science and Technological Development (Serbia), Office of Biological and Environmental Research (Estados Unidos), Australian Research Council, University of Padua (Italia), Swiss National Science Foundation, Institute of Biotechnology, Computational genomics, and Bioinformatics

Subjects
0301 basic medicine, Computer science, Disease gene prioritization, Protein function prediction, Ecology, Evolution, Behavior and Systematics, Genetics, Cell Biology, 05 Environmental Sciences, 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit, computer.software_genre, Quantitative Biology - Quantitative Methods, Wiskundige en Statistische Methoden - Biometris, Field (computer science), Laboratorium voor Plantenveredeling, Function (engineering), Databases, Protein, 1183 Plant biology, microbiology, virology, Quantitative Methods (q-bio.QM), media_common, Genetics & Heredity, Settore BIO/11 - BIOLOGIA MOLECOLARE, Ecology, SISTA, 1184 Genetics, developmental biology, physiology, Life Sciences & Biomedicine, Algorithms, Bioinformatics, Evolution, media_common.quotation_subject, BIOINFORMÁTICA, Machine learning, Bottleneck, Set (abstract data type), BIOS Applied Bioinformatics, 03 medical and health sciences, Annotation, Structure-Activity Relationship, Behavior and Systematics, Human Phenotype Ontology, Humans, ddc:610, DISINTEGRIN, Mathematical and Statistical Methods - Biometris, BIOINFORMATICS, 08 Information And Computing Sciences, Science & Technology, business.industry, Research, ADAM, Proteins, Computational Biology, Molecular Sequence Annotation, 06 Biological Sciences, Data set, ONTOLOGY, Plant Breeding, 030104 developmental biology, Gene Ontology, Biotechnology & Applied Microbiology, FOS: Biological sciences, Artificial intelligence, business, computer, Software
Abstract

BACKGROUND: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. RESULTS: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. CONCLUSIONS: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent., We acknowledge the contributions of Maximilian Hecht, Alexander Grün, Julia Krumhoff, My Nguyen Ly, Jonathan Boidol, Rene Schoeffel, Yann Spöri, Jessika Binder, Christoph Hamm and Karolina Worf. This work was partially supported by the following grants: National Science Foundation grants DBI-1458477 (PR), DBI-1458443 (SDM), DBI-1458390 (CSG), DBI-1458359 (IF), IIS-1319551 (DK), DBI-1262189 (DK), and DBI-1149224 (JC); National Institutes of Health grants R01GM093123 (JC), R01GM097528 (DK), R01GM076990 (PP), R01GM071749 (SEB), R01LM009722 (SDM), and UL1TR000423 (SDM); the National Natural Science Foundation of China grants 3147124 (WT) and 91231116 (WT); the National Basic Research Program of China grant 2012CB316505 (WT); NSERC grant RGPIN 371348-11 (PP); FP7 infrastructure project TransPLANT Award 283496 (ADJvD); Microsoft Research/FAPESP grant 2009/53161-6 and FAPESP fellowship 2010/50491-1 (DCAeS); Biotechnology and Biological Sciences Research Council grants BB/L020505/1 (DTJ), BB/F020481/1 (MJES), BB/K004131/1 (AP), BB/F00964X/1 (AP), and BB/L018241/1 (CD); the Spanish Ministry of Economics and Competitiveness grant BIO2012-40205 (MT); KU Leuven CoE PFV/10/016 SymBioSys (YM); the Newton International Fellowship Scheme of the Royal Society grant NF080750 (TN). CSG was supported in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative grant GBMF4552. Computational resources were provided by CSC – IT Center for Science Ltd., Espoo, Finland (TS). This work was supported by the Academy of Finland (TS). RCL and ANM were supported by British Heart Foundation grant RG/13/5/30112. PD, RCL, and REF were supported by Parkinson’s UK grant G-1307, the Alexander von Humboldt Foundation through the German Federal Ministry for Education and Research, Ernst Ludwig Ehrlich Studienwerk, and the Ministry of Education, Science and Technological Development of the Republic of Serbia grant 173001. This work was a Technology Development effort for ENIGMA – Ecosystems and Networks Integrated with Genes and Molecular Assemblies (http://enigma.lbl.gov), a Scientific Focus Area Program at Lawrence Berkeley National Laboratory, which is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research grant DE-AC02-05CH11231. ENIGMA only covers the application of this work to microbial proteins. NSF DBI-0965616 and Australian Research Council grant DP150101550 (KMV). NSF DBI-0965768 (ABH). NIH T15 LM00945102 (training grant for CSF). FP7 FET grant MAESTRA ICT-2013-612944 and FP7 REGPOT grant InnoMol (FS). NIH R01 GM60595 (PCB). University of Padova grants CPDA138081/13 (ST) and GRIC13AAI9 (EL). Swiss National Science Foundation grant 150654 and UK BBSRC grant BB/M015009/1 (COD). PRB2 IPT13/0001 - ISCIII-SGEFI / FEDER (JMF)., This is the final version of the article. It first appeared from BioMed Central at http://dx.doi.org/10.1186/s13059-016-1037-6.

Published
2016

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