A structure-based Multiple-Instance Learning approach to predicting in vitrotranscription factor-DNA interaction

Zhen Gao, Jianhua Ruan

    Research output: Contribution to journalArticlepeer-review

    4 Scopus citations

    Abstract

    Background: Understanding the mechanism of transcriptional regulation remains an inspiring stage of molecular biology. Recently, in vitro protein-binding microarray experiments have greatly improved the understanding of transcription factor-DNA interaction. We present a method - MIL3D - which predicts in vitro transcription factor binding by multiple-instance learning with structural properties of DNA. Results: Evaluation on in vitro data of twenty mouse transcription factors shows that our method outperforms a method based on simple-instance learning with DNA structural properties, and the widely used k-mer counting method, for nineteen out of twenty of the transcription factors. Our analysis showed that the MIL3D approach can utilize subtle structural similarities when a strong sequence consensus is not available. Conclusion: Combining multiple-instance learning and structural properties of DNA has promising potential for studying biological regulatory networks.

    Original languageEnglish (US)
    Article numberS3
    JournalBMC genomics
    Volume16
    Issue number4
    DOIs
    StatePublished - Apr 21 2015

    ASJC Scopus subject areas

    • Biotechnology
    • Genetics

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