A Drug-target Association Prediction Algorithm with Multi-information Fusion

doi:10.3969/j.issn.1000-3428.2016.06.039

Abstract

Abstract: This paper considers complexity of the classifier and the geometric distribution of data, and proposes a semi-supervised learning algorithm to predict the associations between drugs and targets combining drug-target interactions network based on drug structure similarity and target sequence similarities. Experimental results show that, this algorithm has better prediction performance compared with DBSI algorithm, KBMF2K algorithm, etc.. The drugs-target interaction data predicted by the proposed algorithm is scored and sorted, and parts of interactions data can be retired from KEGG, DrugBank, SuperTarget and ChEMBL among the predicted interactions with 30% highest scores.

Key words: multi-information fusion, semi-supervised learning, drug-target interactions network, drug similarity, target similarity

摘要： 在药物结构相似性和靶标序列相似性的基础上,结合药物-靶标相互作用网络信息,考虑分类器和数据集合分布的复杂性,提出一种半监督学习算法预测药物与靶标之间的关联。实验结果表明,该算法的预测性能较DBSI,KBMF2K等算法有所提高。对其预测到的药物-靶标相互作用数据进行打分并排序,从中提取前30%的数据,其中有部分相互作用可在KEGG,DrugBank,SuperTarget和ChEMBL数据库中得到验证。

关键词: 多信息融合, 半监督学习, 药物-靶标相互作用网络, 药物相似性, 靶标相似性

CLC Number:

TP39

PENG Lihong,LI Zejun,CHEN Min2,REN Rili. A Drug-target Association Prediction Algorithm with Multi-information Fusion[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2016.06.039.

彭利红,李泽军,陈敏,任日丽. 一种多信息融合的药物-靶标关联预测算法[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2016.06.039.

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References

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