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NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning
Chen, Xing; Ren, Biao; Chen, Ming; Wang, Quanxin; Zhang, Lixin; Yan, Guiying; Zhang, LX (reprint author), Chinese Acad Sci, Key Lab Pathogen Microbiol & Immunol, Inst Microbiol, Beijing, Peoples R China.; Zhang, LX (reprint author), Chinese Acad Sci, South China Sea Inst Oceanol, Guangzhou, Guangdong, Peoples R China.
2016
Source PublicationPLOS COMPUTATIONAL BIOLOGY
Volume12Issue:7Pages:e1004975
AbstractFungal 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.
Department[Chen, Xing] China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou, Peoples R China; [Ren, Biao; Chen, Ming; Wang, Quanxin; Zhang, Lixin] Chinese Acad Sci, Key Lab Pathogen Microbiol & Immunol, Inst Microbiol, Beijing, Peoples R China; [Ren, Biao] Sichuan Univ, West China Hosp Stomatol, State Key Lab Oral Dis, Chengdu, Peoples R China; [Wang, Quanxin] Univ Chinese Acad Sci, Beijing, Peoples R China; [Zhang, Lixin] Chinese Acad Sci, South China Sea Inst Oceanol, Guangzhou, Guangdong, Peoples R China; [Yan, Guiying] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China ; LMB
Subject AreaBiochemistry & Molecular Biology ; Mathematical & Computational Biology
Document Type期刊论文
Identifierhttp://ir.scsio.ac.cn/handle/344004/15383
Collection中科院海洋生物资源可持续利用重点实验室
Corresponding AuthorZhang, LX (reprint author), Chinese Acad Sci, Key Lab Pathogen Microbiol & Immunol, Inst Microbiol, Beijing, Peoples R China.; Zhang, LX (reprint author), Chinese Acad Sci, South China Sea Inst Oceanol, Guangzhou, Guangdong, Peoples R China.
Recommended Citation
GB/T 7714
Chen, Xing,Ren, Biao,Chen, Ming,et al. NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning[J]. PLOS COMPUTATIONAL BIOLOGY,2016,12(7):e1004975.
APA Chen, Xing.,Ren, Biao.,Chen, Ming.,Wang, Quanxin.,Zhang, Lixin.,...&Zhang, LX .(2016).NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning.PLOS COMPUTATIONAL BIOLOGY,12(7),e1004975.
MLA Chen, Xing,et al."NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning".PLOS COMPUTATIONAL BIOLOGY 12.7(2016):e1004975.
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