A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data.

ABSTRACT:

Background

The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.

Results

In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-base

SUBMITTER: Costa PR 

PROVIDER: S-EPMC3045802 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature