Biological networks are powerful resources for the discovery of genes and genetic modules
that drive disease. Fundamental to network analysis is the concept that genes underlying the …

High-throughput experimental technologies are generating increasingly massive and
complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to …

Abstract Background The Critical Assessment of Functional Annotation (CAFA) is an
ongoing, global, community-driven effort to evaluate and improve the computational …

Liquid chromatography–high-resolution mass spectrometry (LC-MS)-based metabolomics
aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified …

Automated annotation of protein function is challenging. As the number of sequenced
genomes rapidly grows, the overwhelming majority of protein products can only be …

Networks are ubiquitous in science and have become a focal point for discussion in
everyday life. Formal statistical models for the analysis of network data have emerged as a …

Background: Most successful computational approaches for protein function prediction
integrate multiple genomics and proteomics data sources to make inferences about the …

Functional annotation of proteins is a fundamental problem in the post‐genomic era. The
recent availability of protein interaction networks for many model species has spurred on the …

Protein–protein interactions (PPIs) and their networks play a central role in all biological
processes. Akin to the complete sequencing of genomes and their comparative analysis …

Recent genome sequencing studies have shown that the somatic mutations that drive
cancer development are distributed across a large number of genes. This mutational …