Citation: Genome Biology 2008 9(Suppl 1):S1
Volume 9 Supplement 1
Quantitative inference of gene function from diverse large-scale datasets
Research
Edited by 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...
Citation: Genome Biology 2008 9(Suppl 1):S2
-
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...
Citation: Genome Biology 2008 9(Suppl 1):S3
-
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 ...
Citation: Genome Biology 2008 9(Suppl 1):S4
-
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...
Citation: Genome Biology 2008 9(Suppl 1):S5
-
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...
Citation: Genome Biology 2008 9(Suppl 1):S6
-
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...
Citation: Genome Biology 2008 9(Suppl 1):S7
-
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...
Citation: Genome Biology 2008 9(Suppl 1):S8