bioRxiv preprint doi: https://doi.org/10.1101/100305; this version posted January 13, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information Yunan Luo1,3,y, Xinbin Zhao2,y, Jingtian Zhou2,y, Jinling Yang1, Yanqing Zhang1, Wenhua Kuang2, Jian Peng3,*, Ligong Chen2,*, and Jianyang Zeng1,* 1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China 2School of Pharmaceutical Sciences, Tsinghua University, Beijing, China 3Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA yThese authors contributed equally to this work *Corresponding authors:
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[email protected] Abstract The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. Systematic integration of these heterogeneous data not only serves as a promising tool for identifying new drug-target interactions (DTIs), which is an important step in drug development, but also provides a more complete understanding of the molecular mechanisms of drug action. In this work, we integrate diverse drug-related information, including drugs, proteins, diseases and side-effects, together with their interactions, associations or similarities, to construct a heterogeneous network with 12,015 nodes and 1,895,445 edges. We then develop a new computational pipeline, called DTINet, to predict novel drug-target interactions from the constructed heterogeneous network.