human disease-drug network based on genomic expression profiles基于基因表达谱的人类disease-drug网络.pdfVIP

Human Disease-Drug Network Based on Genomic Expression Profiles Guanghui Hu*, Pankaj Agarwal Computational Biology, GlaxoSmithKline, King of Prussia, Pennsylvania, United States of America Abstract Background: Drug repositioning offers the possibility of faster development times and reduced risks in drug discovery. With the rapid development of high-throughput technologies and ever-increasing accumulation of whole genome-level datasets, an increasing number of diseases and drugs can be comprehensively characterized by the changes they induce in gene expression, protein, metabolites and phenotypes. Methodology/Principal Findings: We performed a systematic, large-scale analysis of genomic expression profiles of human diseases and drugs to create a disease-drug network. A network of 170,027 significant interactions was extracted from the ,24.5 million comparisons between ,7,000 publicly available transcriptomic profiles. The network includes 645 disease- disease, 5,008 disease-drug, and 164,374 drug-drug relationships. At least 60% of the disease-disease pairs were in the same disease area as determined by the Medical Subject Headings (MeSH) disease classification tree. The remaining can drive a molecular level nosology by discovering relationships between seemingly unrelated diseases, such as a connection between bipolar disorder and hereditary spastic paraplegia, and a connection between actinic keratosis and cancer. Among the 5,008 disease-drug links, connections with negative scores suggest new indications for existing drugs, such as the use of some antimalaria drugs for Crohn’s disease, and a variety of existing drugs for Huntington’s disease; while the positive scoring connections can aid in drug side effect identification, such as tamoxifen’s undesired carcinogenic property. From the ,37K drug-drug relationships, we discover relationships that aid in target and pa