Artif Life Robotics (2014) 19:406–413 DOI 10.1007/s10015-014-0178-5 1 3 ORIGINAL ARTICLE Identification of drug-target modules in the human protein–protein interaction network Takeshi Hase · Kaito Kikuchi · Samik Ghosh · Hiroaki Kitano · Hiroshi Tanaka Received: 22 July 2014 / Accepted: 15 October 2014 / Published online: 12 November 2014 © ISAROB 2014 identified drug-target modules that contain more than 40 % of targets of cancer molecular-targeted drugs (e.g., kinase inhibitors and monoclonal antibodies). Furthermore, pro- teins in the modules are significantly involved in cancer- related signaling pathways (e.g., vascular endothelial growth factor signaling pathway). These results indicate that the listing of proteins and interactions in the drug- target modules may help us to search efficiently for drug action mechanisms and novel candidate targets for cancer- ous diseases. It may be pertinent to note here that, among proteins in the drug-target modules, proteins with a small number of interactions may be potential candidate anti-can- cer targets with less severe side effects. Keywords Protein–Protein interaction · Module · Drug target · Cancer · Network analysis Abstract The human protein–protein interaction net- work (PIN) has a modular structure, in which interactions between proteins are much denser within the same mod- ule than between different modules. Proteins within the same module tend to have closely related functions with each other. Therefore, if a module is composed of rela- tively small number of proteins (e.g., modules composed of less than 5 % of all proteins in the PIN) and significantly enriched with target proteins for a disease, proteins and interactions in the module are likely to play an important role in disease mechanisms and may be potential candidate targets for the disease. We defined such modules as “drug- target modules.” In order to find drug-target modules in the human PIN, we developed a novel computational approach that decomposes the network into small modules and maps drug targets on the modules. The approach successfully This work was presented in part at the 19th International Symposium on Artificial Life and Robotics, Beppu, Oita, January 22–24, 2014. T. Hase (*) · K. Kikuchi · S. Ghosh · H. Kitano (*) The Systems Biology Institute, Falcon Building 5F, 5-6-9 Shirokanedai, Minato, Tokyo 108-0071, Japan e-mail: ht.bioinfo.tmd@gmail.com H. Kitano e-mail: kitano@sbi.jp T. Hase · S. Ghosh · H. Kitano Laboratory of Disease Systems Modeling, Center for Integrative Medical Sciences, RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan T. Hase · H. Tanaka (*) Department of Bioinformatics, Medical Research Institute, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan e-mail: tanaka@bioinfo.tmd.ac.jp H. Kitano Sony Computer Science Laboratories, Inc., Takanawa Muse Building 3F, 3-14-13, Higashigotanda, Shinagawa-Ku, Tokyo 141-0022, Japan H. Kitano Okinawa Institute of Science and Technology, 7542 Onna, Onna-son, Kunigami, Okinawa 904-0411, Japan H. Tanaka Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-Ku, Sendai 980-8573, Japan