A pathway-based view of human diseases and disease relationships.
It is increasingly evident that human diseases are not isolated from each other. Understanding how different diseases are related to each other based on the underlying biology could provide new insights into disease etiology, classification, and shared biological mechanisms. We have taken a computational approach to studying disease relationships through 1) systematic identification of disease associated genes by literature mining, 2) associating diseases to biological pathways where disease genes are enriched, and 3) linking diseases together based on shared pathways. We identified 4,195 candidate disease associated genes for 1028 diseases. On average, about 50% of disease associated genes of a disease are statistically mapped to pathways. We generated a disease network which consists of 591 diseases and 6,931 disease relationships. We examined properties of this network and provided examples of novel disease relationships which cannot be readily captured through simple literature search or gene overlap analysis. Our results could potentially provide insights into the design of novel, pathway-guided therapeutic interventions for diseases.
Li
Yong
Y
Computational Biology, GlaxoSmithKline R&D, King of Prussia, Pennsylvania, United States of America. yong.2.li@gsk.com
Agarwal
Pankaj
P
D004194:Disease / Q000235:genetics*; D053263:Gene Regulatory Networks; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics*