Semi-supervised Drug-Protein Interaction Prediction from Heterogeneous Spaces

摘要：

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interaction while myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often gives better results than using the labeled data alone. Further, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. We report encouraging results of our method on drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

关键词： Drug-Protein Interaction Network Semi-supervised Learning Kernel Methods Norrealized Laplacian

作者: Zheng Xia Xiaobo Zhou Youxian Sun Ling-Yun Wu

作者单位: State Key Lab of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China Center for Center for Biotechnology & Informatics and Department of Radiology,The Methodist Hospital Reseach In State Key Lab of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China Institute of Applied Mathematics,Academy of Mathematics and Systems Science,Chinese Academy of Scien

会议类型: 国际会议

会议名称: The 3rd International Symposium on Optimization and System Biology(第三届最优化与系统生物学国际会议 OSB09)

会议地点: 张家界

会议语种:英文

页码: 123-131

在线出版日期: 2009-09-20（万方平台首次上网日期，不代表论文的发表时间）