NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning
文献类型：期刊论文
| 作者 | Chen, Xing1; Ren, Biao2,3; Chen, Ming2; Wang, Quanxin2,4; Zhang, Lixin2,5; Yan, Guiying6
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| 刊名 | PLOS COMPUTATIONAL BIOLOGY
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| 出版日期 | 2016-07-01 |
| 卷号 | 12期号:7页码:23 |
| ISSN号 | 1553-734X |
| DOI | 10.1371/journal.pcbi.1004975 |
| 英文摘要 | 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. |
| 资助项目 | National Natural Science Foundation of China[11301517] ; National Natural Science Foundation of China[11371355] ; National Natural Science Foundation of China[10531070] ; National Natural Science Foundation of China[10721101] ; National Natural Science Foundation of China[30973665] ; National Natural Science Foundation of China[30700015] ; National Natural Science Foundation of China[30901849] ; National Natural Science Foundation of China[30910376] ; National Natural Science Foundation of China[30911120484] ; National Natural Science Foundation of China[81011120046] ; National Natural Science Foundation of China[30911120483] ; National 863 Project[2006AA09Z402] ; National 863 Project[2007AA09Z443] ; Key Project for International Cooperation[2007DFB31620] ; National Key Technology RD Program[2007BAI26B02] ; CAS Pillar Program[KSCX2-YW-R-164] ; Important National Science & Technology Specific Projects[2008ZX09401-05] ; Important National Science & Technology Specific Projects[2009ZX09302-004] ; National Center for Mathematics and Interdisciplinary Sciences, CAS |
| WOS研究方向 | Biochemistry & Molecular Biology ; Mathematical & Computational Biology |
| 语种 | 英语 |
| WOS记录号 | WOS:000383351400013 |
| 出版者 | PUBLIC LIBRARY SCIENCE |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/23686]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Zhang, Lixin |
| 作者单位 | 1.China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou, Peoples R China 2.Chinese Acad Sci, Key Lab Pathogen Microbiol & Immunol, Inst Microbiol, Beijing, Peoples R China 3.Sichuan Univ, West China Hosp Stomatol, State Key Lab Oral Dis, Chengdu, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Chinese Acad Sci, South China Sea Inst Oceanol, Guangzhou, Guangdong, Peoples R China 6.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China |
| 推荐引用方式 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):23. |
| APA | Chen, Xing,Ren, Biao,Chen, Ming,Wang, Quanxin,Zhang, Lixin,&Yan, Guiying.(2016).NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning.PLOS COMPUTATIONAL BIOLOGY,12(7),23. |
| MLA | Chen, Xing,et al."NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning".PLOS COMPUTATIONAL BIOLOGY 12.7(2016):23. |
入库方式： OAI收割
来源：数学与系统科学研究院
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