Prediction of candidate genes for neuropsychiatric disorders using feature-based enrichment
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
A comparative study of covariance selection models for the inference of gene regulatory networks
Journal of Biomedical Informatics
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Motivation: Understanding the full meaning of the biology captured in molecular profiles, within the context of the entire biological system, cannot be achieved with a simple examination of the individual genes in the signature. To facilitate such an understanding, we have developed GATHER, a tool that integrates various forms of available data to elucidate biological context within molecular signatures produced from high-throughput post-genomic assays. Results: Analyzing the Rb/E2F tumor suppressor pathway, we show that GATHER identifies critical features of the pathway. We further show that GATHER identifies common biology in a series of otherwise unrelated gene expression signatures that each predict breast cancer outcome. We quantify the performance of GATHER and find that it successfully predicts 90% of the functions over a broad range of gene groups. We believe that GATHER provides an essential tool for extracting the full value from molecular signatures generated from genome-scale analyses. Availability: GATHER is available at http://gather.genome.duke.edu/ Contact: j.nevins@duke.edu Supplementary information: Supplementary data are available at Bioinformatics online.