Computational functional genomics-based reduction of disease-related gene sets to their key components.

摘要: MOTIVATION The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number candidates. Although expression based methods gene set reduction are applied to laboratory-derived data, the analysis topical sets genes gathered from knowledge bases requires a modified approach as no quantitative information about is available. RESULTS We propose computational functional genomics-based at reducing most relevant items on importance within polyhierarchy biological processes characterizing disease. Knowledge roles can provide valid description traits or represented directed acyclic graph (DAG) picturing disease processes. proposed method uses score derived location gene-related DAG. It attempts recreate DAG and thereby, original set, with least descending order importance. obtained precision recall over 70% components charactering functions n=540 pain subset only k=29 best-scoring genes. CONCLUSIONS A new shown that able reproduce which full involved by 70%; however, using ∼5% AVAILABILITY AND IMPLEMENTATION necessary numerical parameters calculation implemented R package dbtORA https://github.com/IME-TMP-FFM/dbtORA. SUPPLEMENTARY INFORMATION Supplementary data available Bioinformatics online.