Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles
文献类型：期刊论文
| 作者 | Li, Xiangyi1,5; Chen, Lanming5; Cui, Hui1; Wang, Disong1; Lian, Baofeng1; Li, Wei1; Qin, Guangrong1; Xie, Lu1; Xu, Yingjie2; Huang, Tao3 |
| 刊名 | ARTIFICIAL INTELLIGENCE IN MEDICINE
 |
| 出版日期 | 2017 |
| 卷号 | 83期号:SI页码:35-43 |
| 关键词 | Synergistic drug combinations Cancer Network features Gene expression profiles Random forest |
| ISSN号 | 0933-3657 |
| DOI | 10.1016/j.artmed.2017.05.008 |
| 文献子类 | Article |
| 英文摘要 | Objective: Synergistic drug combinations are promising therapies for cancer treatment. However, effective prediction of synergistic drug combinations is quite challenging as mechanisms of drug synergism are still unclear. Various features such as drug response, and target networks may contribute to prediction of synergistic drug combinations. In this study, we aimed to construct a computational model to predict synergistic drug combinations. Methods: We designed drug physicochemical features and network features, including drug chemical structure similarity, target distance in protein-protein network and targeted pathway similarity. At the same time, we designed fifteen pharmacogenomics features using drug treated gene expression profiles based on the background of cancer-related biology network. Based on these eighteen features, we built a prediction model for Synergistic Drug combination using Random forest algorithm (SyDRa). Results: Our model achieved a quite good performance with AUC value of 0.89 and Out-of-bag estimate error rate of 0.15 in training dataset. Using the random anti-cancer drug combinations which have transcriptional profile data in the Connectivity Map dataset as the testing dataset, we identified 28 potentially synergistic drug combinations, three out of which had been reported to be effective drug combinations by literatures. Conclusions: We studied eighteen features for drug combinations and built a computational model using random forest algorithm. The model was evaluated using an independent test dataset. Our model provides an efficient strategy to identify potentially synergistic drug combinations for cancer and may help reduce the search space for high-throughput synergistic drug combinations screening. (C) 2017 Elsevier B.V. All rights reserved. |
| 学科主题 | Computer Science ; Engineering ; Medical Informatics |
| WOS关键词 | PHASE-I ; CANCER ; RESISTANCE ; IDENTIFICATION ; TRANSPORTERS ; EXPLORATION ; SIGNATURES ; DISCOVERY ; PARADIGM ; DISEASE |
| 语种 | 英语 |
| WOS记录号 | WOS:000418314700004 |
| 出版者 | ELSEVIER SCIENCE BV |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/1062]  |
| 专题 | 中国科学院上海生命科学研究院营养科学研究所 |
| 作者单位 | 1.Shanghai Acad Sci & Technol, Shanghai Ctr Bioinformat Technol, 1278 Keyuan Rd, Shanghai 201203, Peoples R China; 2.Shanghai Jiao Tong Univ, Sch Med, Tongren Hosp, 1111 Xianxia Rd, Shanghai 200336, Peoples R China; 3.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, 320 Yueyang Rd, Shanghai 200031, Peoples R China; 4.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China, 5.Shanghai Ocean Univ, Key Lab Qual & Safety Risk Assessment Aquat Prod, China Minist Agr, Coll Food Sci & Technol, 999 Hu Cheng Huan Rd, Shanghai 201306, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Li, Xiangyi,Chen, Lanming,Cui, Hui,et al. Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles[J]. ARTIFICIAL INTELLIGENCE IN MEDICINE,2017,83(SI):35-43. |
| APA | Li, Xiangyi.,Chen, Lanming.,Cui, Hui.,Wang, Disong.,Lian, Baofeng.,...&,.(2017).Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles.ARTIFICIAL INTELLIGENCE IN MEDICINE,83(SI),35-43. |
| MLA | Li, Xiangyi,et al."Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles".ARTIFICIAL INTELLIGENCE IN MEDICINE 83.SI(2017):35-43. |
入库方式： OAI收割
来源：上海营养与健康研究所
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