title: NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning.
abstract: Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations.
authors.affiliation: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China.
authors.forename: Xing
authors.initials: X
authors.lastname: Chen
authors.affiliation: Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.|State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Sichuan, China.
authors.forename: Biao
authors.initials: B
authors.lastname: Ren
authors.affiliation: Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Chen
authors.affiliation: Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.|University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Quanxin
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Chinese Academy of Sciences Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.|South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China.
authors.forename: Lixin
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
authors.forename: Guiying
authors.initials: G
authors.lastname: Yan
mesh_terms: D000935:Antifungal Agents / Q000494:pharmacology; D002176:Candida albicans; D019295:Computational Biology / Q000379:methods*; D004338:Drug Combinations; D004357:Drug Synergism; D006801:Humans; D008962:Models, Theoretical; D000069553:Supervised Machine Learning
chemical_list: D000935:Antifungal Agents; D004338:Drug Combinations