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oai:repisalud.isciii.es:20.500.12105/85112024-11-29T15:54:29Zcom_20.500.12105_2173com_20.500.12105_2051col_20.500.12105_19597

00925njm 22002777a 4500 dc Jiang, Yuxiang author Oron, Tal Ronnen author Clark, Wyatt T author Bankapur, Asma R author D'Andrea, Daniel author Lepore, Rosalba author Funk, Christopher S author Kahanda, Indika author Verspoor, Karin M author Ben-Hur, Asa author Koo, Da Chen Emily author Penfold-Brown, Duncan author Shasha, Dennis author Youngs, Noah author Bonneau, Richard author Lin, Alexandra author Sahraeian, Sayed M E author Martelli, Pier Luigi author Profiti, Giuseppe author Casadio, Rita author Cao, Renzhi author Zhong, Zhaolong author Cheng, Jianlin author Altenhoff, Adrian author Skunca, Nives author Dessimoz, Christophe author Dogan, Tunca author Hakala, Kai author Kaewphan, Suwisa author Mehryary, Farrokh author Salakoski, Tapio author Ginter, Filip author Fang, Hai author Smithers, Ben author Oates, Matt author Gough, Julian author Törönen, Petri author Koskinen, Patrik author Holm, Liisa author Chen, Ching-Tai author Hsu, Wen-Lian author Bryson, Kevin author Cozzetto, Domenico author Minneci, Federico author Jones, David T author Chapman, Samuel author Bkc, Dukka author Khan, Ishita K author Kihara, Daisuke author Ofer, Dan author Rappoport, Nadav author Stern, Amos author Cibrian-Uhalte, Elena author Denny, Paul author Foulger, Rebecca E author Hieta, Reija author Legge, Duncan author Lovering, Ruth C author Magrane, Michele author Melidoni, Anna N author Mutowo-Meullenet, Prudence author Pichler, Klemens author Shypitsyna, Aleksandra author Li, Biao author Zakeri, Pooya author ElShal, Sarah author Tranchevent, Léon-Charles author Das, Sayoni author Dawson, Natalie L author Lee, David author Lees, Jonathan G author Sillitoe, Ian author Bhat, Prajwal author Nepusz, Tamás author Romero, Alfonso E author Sasidharan, Rajkumar author Yang, Haixuan author Paccanaro, Alberto author Gillis, Jesse author Sedeño-Cortés, Adriana E author Pavlidis, Paul author Feng, Shou author Cejuela, Juan M author Goldberg, Tatyana author Hamp, Tobias author Richter, Lothar author Salamov, Asaf author Gabaldón, Toni author Marcet-Houben, Marina author Supek, Fran author Gong, Qingtian author Ning, Wei author Zhou, Yuanpeng author Tian, Weidong author Falda, Marco author Fontana, Paolo author Lavezzo, Enrico author Toppo, Stefano author Ferrari, Carlo author Giollo, Manuel author Piovesan, Damiano author Tosatto, Silvio C E author Del Pozo, Angela author Fernández, José M author Maietta, Paolo author Valencia, Alfonso author Tress, Michael L author Benso, Alfredo author Di Carlo, Stefano author Politano, Gianfranco author Savino, Alessandro author Rehman, Hafeez Ur author Re, Matteo author Mesiti, Marco author Valentini, Giorgio author Bargsten, Joachim W author van Dijk, Aalt D J author Gemovic, Branislava author Glisic, Sanja author Perovic, Vladmir author Veljkovic, Veljko author Veljkovic, Nevena author Almeida-E-Silva, Danillo C author Vencio, Ricardo Z N author Sharan, Malvika author Vogel, Jörg author Kansakar, Lakesh author Zhang, Shanshan author Vucetic, Slobodan author Wang, Zheng author Sternberg, Michael J E author Wass, Mark N author Huntley, Rachael P author Martin, Maria J author O'Donovan, Claire author Robinson, Peter N author Moreau, Yves author Tramontano, Anna author Babbitt, Patricia C author Brenner, Steven E author Linial, Michal author Orengo, Christine A author Rost, Burkhard author Greene, Casey S author Mooney, Sean D author Friedberg, Iddo author Radivojac, Predrag author 2016-09-07 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. Genome Biol. 2016;17(1):184. 10.1186/s13059-016-1037-6 1474-760X 1474-760X Genome biology 27604469 http://hdl.handle.net/20.500.12105/8511 Disease gene prioritization Protein function prediction An expanded evaluation of protein function prediction methods shows an improvement in accuracy