A hadoop-based method to predict potential effective drug combination.
A hadoop-based method to predict potential effective drug combination.
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DOI：
10.1155/2014/196858
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发表时间：
2014
影响因子：
--
通讯作者：
Wei D
中科院分区：
文献类型：
--
作者：
Sun Y;Xiong Y;Xu Q;Wei D
同时影响多个靶点的联合药物因其疗效提高和副作用减少而成为治疗复杂疾病的有希望的候选者。然而，对所有可能的药物组合进行详尽的筛选是极其耗时和不切实际的。在这里，我们提出了一种新的基于hadoop的方法，利用MapReduce编程模型来预测药物组合，从而提高了预测算法的可扩展性。通过整合多种药物的基因表达数据，构建数据预处理，并在Hadoop上构建支持向量机和naïve贝叶斯分类器进行药物组合预测。实验结果表明，基于hadoop的模型在大数据处理步骤中效率更高，性能令人满意。我们相信，我们提出的方法可以帮助未来随着组合数量以指数速度增加，加速对潜在有效药物的预测。源代码和数据集可按要求提供。
Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request.
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影响因子：
--
作者：
Wu Z;Zhao XM;Chen L
通讯作者：
Chen L
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
影响因子：
--
作者：
Chen L;Li BQ;Zheng MY;Zhang J;Feng KY;Cai YD
通讯作者：
Cai YD
影响因子：
3.1
作者：
Wang Yanjuan;Fang Yanxiong;Liu Chunying
通讯作者：
Liu Chunying
影响因子：
2.1
作者：
Irizarry, RA;Hobbs, B;Speed, TP
通讯作者：
Speed, TP
DCDB：药物组合数据库
影响因子：
5.8
作者：
Liu, Yanbin;Hu, Bin;Chen, Xin
通讯作者：
Chen, Xin