Prediction of gene-based drug indications using compendia of public gene expression data and PubMed abstracts.

The tremendous research effort on diseases and drug discovery has produced a huge amount of important biomedical information which is mostly hidden in the web. In addition, many databases have been created for the purpose of storing enormous amounts of information and high-throughput experiments related to drugs and diseases' effects on genes. Thus, developing an algorithm to integrate biological data from different sources forms one of the greatest challenges in the field of computational biology. Based on our belief that data integration would result in better understanding for the drug mode of action or the disease pathophysiology, we have developed a novel paradigm to integrate data from three major sources in order to predict novel therapeutic drug indications. Microarray data, biomedical text mining data, and gene interaction data have been all integrated to predict ranked lists of genes based on their relevance to a particular drug or disease molecular action. These ranked lists of genes have finally been used as a raw material for building a disease-drug connectivity map based on the enrichment between the up/down tags of a particular disease signature and the ranked lists of drugs. Using this paradigm, we have reported 13% sensitivity improvement in comparison with using microarray or text mining data independently. In addition, our paradigm is able to predict many clinically validated disease-drug associations that could not be captured using microarray or text mining data independently.