2024-03-29T10:05:38Zhttp://repisalud.isciii.es/oai/requestoai:repisalud.isciii.es:20.500.12105/85112023-10-05T07:20:33Zcom_20.500.12105_2174com_20.500.12105_2051com_20.500.12105_2173col_20.500.12105_2175
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.
1474-760X
http://hdl.handle.net/20.500.12105/8511
27604469
10.1186/s13059-016-1037-6
1474-760X
Genome biology
Disease gene prioritization
Protein function prediction
An expanded evaluation of protein function prediction methods shows an improvement in accuracy