Prediction of compound-protein interactions with machine learning methods

Yoshihiro Yamanishi, Hisashi Kashima

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.

    Original languageEnglish
    Title of host publicationChemoinformatics and Advanced Machine Learning Perspectives
    Subtitle of host publicationComplex Computational Methods and Collaborative Techniques
    PublisherIGI Global
    Pages304-317
    Number of pages14
    ISBN (Print)9781615209118
    DOIs
    Publication statusPublished - 2010

    All Science Journal Classification (ASJC) codes

    • Agricultural and Biological Sciences(all)

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