Prediction of effective drug combinations by chemical interaction, protein interaction and target enrichment of KEGG pathways.
Prediction of effective drug combinations by chemical interaction, protein interaction and target enrichment of KEGG pathways.
复制标题
通过化学相互作用、蛋白质相互作用和 KEGG 通路靶点富集预测有效药物组合
DOI：
10.1155/2013/723780
复制
发表时间：
2013
影响因子：
--
通讯作者：
Cai YD
中科院分区：
文献类型：
--
作者：
Chen L;Li BQ;Zheng MY;Zhang J;Feng KY;Cai YD
药物联合治疗由于疗效好、副作用小，在治疗某些复杂疾病方面比单一药物治疗更有效。尽管正在使用一些药物组合，但其潜在的分子机制仍然知之甚少。因此，通过它们的分子机制，以稳健和严谨的方式推断出一种新的药物组合是非常有意义的。本文试图通过综合考虑：(1)药物之间的化学相互作用，(2)药物靶点之间的蛋白质相互作用，(3)KEGG途径的靶点富集来预测有效的药物组合。构建了一个基准数据集，包含121个已确认的有效组合和605个随机组合。每种药物组合由上述三个性质衍生的465个特征表示。采用最小冗余、最大关联和增量特征选择技术提取关键特征。建立随机森林模型，并通过5次交叉验证对其性能进行评价。最终选出了55个提供最佳预测结果的关键特征。这些重要特征可能有助于深入了解药物联合的机制，并且所提出的预测模型可能成为筛选可能的药物联合的有用工具。
Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs' targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.
登录
查看更多内容
影响因子：
3.7
作者：
Hu L;Huang T;Shi X;Lu WC;Cai YD;Chou KC
通讯作者：
Chou KC
影响因子：
2.4
作者：
Huang, N;Chen, H;Sun, ZR
通讯作者：
Sun, ZR
影响因子：
56.9
作者：
Campillos, Monica;Kuhn, Michael;Bork, Peer
通讯作者：
Bork, Peer
影响因子：
6.7
作者：
Howden BP;McEvoy CR;Allen DL;Chua K;Gao W;Harrison PF;Bell J;Coombs G;Bennett-Wood V;Porter JL;Robins-Browne R;Davies JK;Seemann T;Stinear TP
通讯作者：
Stinear TP
影响因子：
4.8
作者：
Gourbal, B;Sonuc, N;Mukhopadhyay, R
通讯作者：
Mukhopadhyay, R