Work

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

Year: 2010

Type: article

Abstract: BACKGROUND: 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 interactions and 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 generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. RESULTS: Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG. CONCLUSIONS: We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

Source: BMC Systems Biology

DOI: 10.1186/1752-0509-4-s2-s6

Authors: Zheng Xia, Ling-Yun Wu, Xiaobo Zhou, Stephen TC Wong

Institutions: Houston Methodist, Cornell University, Chinese Academy of Sciences, Academy of Mathematics and Systems Science

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,290