Quantitative inference of gene function from diverse large-scale datasets

A race through the maze of genomic evidence

Authors: Timothy R Hughes and Frederick P Roth

A critical assessment of Mus musculusgene function prediction using integrated genomic evidence

Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help...

Authors: Lourdes Peña-Castillo, Murat Tasan, Chad L Myers, Hyunju Lee, Trupti Joshi, Chao Zhang, Yuanfang Guan, Michele Leone, Andrea Pagnani, Wan Kyu Kim, Chase Krumpelman, Weidong Tian, Guillaume Obozinski, Yanjun Qi, Sara Mostafavi, Guan Ning Lin…

Predicting gene function in a hierarchical context with an ensemble of classifiers

The wide availability of genome-scale data for several organisms has stimulated interest in computational approaches to gene function prediction. Diverse machine learning methods have been applied to unicellul...

Authors: Yuanfang Guan, Chad L Myers, David C Hess, Zafer Barutcuoglu, Amy A Caudy and Olga G Troyanskaya

GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function

Most successful computational approaches for protein function prediction integrate multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. The most accurate of ...

Authors: Sara Mostafavi, Debajyoti Ray, David Warde-Farley, Chris Grouios and Quaid Morris

Inferring mouse gene functions from genomic-scale data using a combined functional network/classification strategy

The complete set of mouse genes, as with the set of human genes, is still largely uncharacterized, with many pieces of experimental evidence accumulating regarding the activities and expression of the genes, b...

Authors: Wan Kyu Kim, Chase Krumpelman and Edward M Marcotte

Consistent probabilistic outputs for protein function prediction

In predicting hierarchical protein function annotations, such as terms in the Gene Ontology (GO), the simplest approach makes predictions for each term independently. However, this approach has the unfortunate...

Authors: Guillaume Obozinski, Gert Lanckriet, Charles Grant, Michael I Jordan and William Stafford Noble

Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiaegene function

Learning the function of genes is a major goal of computational genomics. Methods for inferring gene function have typically fallen into two categories: 'guilt-by-profiling', which exploits correlation between...

Authors: Weidong Tian, Lan V Zhang, Murat Taşan, Francis D Gibbons, Oliver D King, Julie Park, Zeba Wunderlich, J Michael Cherry and Frederick P Roth

An en masse phenotype and function prediction system for Mus musculus

Individual researchers are struggling to keep up with the accelerating emergence of high-throughput biological data, and to extract information that relates to their specific questions. Integration of accumula...

Authors: Murat Taşan, Weidong Tian, David P Hill, Francis D Gibbons, Judith A Blake and Frederick P Roth