Paper39-Pathway-based-view-human-diseases-disease-relationship-PLOS-One-4(2)-e4346-doi-10-1371-Li-an

Paper39-Pathway-based-view-human-diseases-disease-relationship-PLOS-One-4(2)-e4346-doi-10-1371-Li-an

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A Pathway-Based View of Human Diseases and Disease Relationships Yong Li * , Pankaj Agarwal Computational Biology, GlaxoSmithKline R&D, King of Prussia, Pennsylvania, United States of America Abstract 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. Citation: Li Y, Agarwal P (2009) A Pathway-Based View of Human Diseases and Disease Relationships. PLoS ONE 4(2): e4346. doi:10.1371/journal.pone.0004346 Editor: Winston Hide, University of the Western Cape, South Africa Received December 3, 2008; Accepted December 11, 2008; Published February 4, 2009 Copyright: ß 2009 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist. * E-mail: yong.2.li@gsk.com Introduction The combination of genetics and molecular biology has greatly facilitated the identification of candidate genes for human diseases [1,2]. More recently, with the completion of human genome sequencing, genome-wide association, transcriptomic and proteo- mic expression studies further accelerated the pace of disease gene hunt [3–6]. It has become evident that very often multiple genes collectively contribute to the etiology and clinical manifestations of human diseases including both classic Mendelian diseases and complex diseases such as T2DM and cancers [7]. Understanding how different diseases relate to each other will not only provide us with a global view of human diseasome, but also provide potentially new insights into the etiology, classification, and design of novel therapeutic interventions. Network biology has been proposed as a platform to understand relationships among disease genes and how they contribute to clinical phenotypes [7,8]. Goh et. al have taken a step to study relationships among diseases by
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This note was uploaded on 07/25/2011 for the course EMA 6580 taught by Professor Staff during the Spring '08 term at University of Florida.

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Paper39-Pathway-based-view-human-diseases-disease-relationship-PLOS-One-4(2)-e4346-doi-10-1371-Li-an

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