Computational model for disease research.

Google Scholar Crossref Search ADS PubMed WorldCat 12 Moreau Y, Tranchevent L-C. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Computational analysis of vast public and private omics data [[1], [3], [5]] generated by high-throughput technologies [[6]] aids in deciphering complex mechanisms [[8], [10]] and relevant gene functions [[12], [14]] of various diseases ranging from viral infections to cancers. The tool contained automated analytical pipelines to match disease and gene IDs, conduct enrichment analysis, compare diseases' shared genes and variants, estimate background distribution, and examine the statistical significance of candidate disease mechanisms by calculate empirical I P i -values that quantified disease functional and genetic coherence. Google Scholar Crossref Search ADS PubMed WorldCat 20 Zhao X, Liu T, Wang G. Ensemble classification based signature discovery for cancer diagnosis in RNA expression profiles across different platforms. [Extracted from the article]