title: Inferring novel indications of approved drugs via a learning method with local and global consistency.
abstract: Inferring new indication of approved drugs is critical not only for the elucidation of the interaction mechanisms between these drugs and their associated diseases, but also for the development of drug therapy for various human diseases. This paper proposes a network-based approach to reveal the association between 52 human diseases and potential therapeutic drugs based on multiple types of data. The advantage of the approach is that it can obtain the global relevance features for each drug-disease pair in the network by the learning local and global consistency method (LLGC). Cross-validation tests results demonstrate the proposed approach can achieve better performance comparing with previous methods. More importantly, it provides a promising strategy to maximize the value of therapeutic drugs and offer safe and effective treatments for different diseases.
authors.affiliation: Department of Computer Science & Technology, East China Normal University, Shanghai, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Yan
authors.affiliation: Department of Mathematics, East China Normal University, Shanghai, China.
authors.forename: Xinwei
authors.initials: X
authors.lastname: Shao
authors.affiliation: Department of Computer Science & Technology, East China Normal University, Shanghai, China.
authors.forename: Zhenran
authors.initials: Z
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000494:pharmacology / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy; D019295:Computational Biology; D004353:Drug Evaluation, Preclinical; D058492:Drug Repositioning; D005260:Female; D006801:Humans
chemical_list: D000970:Antineoplastic Agents