Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network
Abstract
// Hao Huang 1, * , Yuehan He 1, * , Wan Li 1 , Wenqing Wei 1 , Yiran Li 1 , Ruiqiang Xie 1 , Shanshan Guo 1 , Yahui Wang 1 , Jing Jiang 1 , Binbin Chen 1 , Junjie Lv 1 , Nana Zhang 2 , Lina Chen 1 , Weiming He 3 1 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China 2 Department of Endocrinology and Metabolism Laboratory, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China 3 Institute of Opto-electronics, Harbin Institute of Technology, Harbin, Heilongjiang Province, China * These authors have contributed equally to this work Correspondence to: Lina Chen, email: [email protected] Weiming He, email: [email protected] Keywords: polycystic ovary syndrome, protein-protein interaction network, pathobiological similarity, module, drug target Received: January 04, 2016 Accepted: April 28, 2016 Published: May 13, 2016 ABSTRACT Polycystic ovary syndrome (PCOS) is one of the most common endocrinological disorders in reproductive aged women. PCOS and Type 2 Diabetes (T2D) are closely linked in multiple levels and possess high pathobiological similarity. Here, we put forward a new computational approach based on the pathobiological similarity to identify PCOS potential drug target modules (PPDT-Modules) and PCOS potential drug targets in the protein-protein interaction network (PPIN). From the systems level and biological background, 1 PPDT-Module and 22 PCOS potential drug…
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