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

Zheng Xia, Ling Yun Wu, Xiaobo Zhou, Stephen T.C. Wong

    Research output: Contribution to journalArticlepeer-review

    304 Scopus citations

    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.

    Original languageEnglish (US)
    Article number6
    JournalBMC systems biology
    Volume4
    Issue numberSUPPL. 2
    DOIs
    StatePublished - 2010

    ASJC Scopus subject areas

    • Structural Biology
    • Modeling and Simulation
    • Molecular Biology
    • Computer Science Applications
    • Applied Mathematics

    Fingerprint

    Dive into the research topics of 'Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces'. Together they form a unique fingerprint.

    Cite this