An iterated conditional modes solution for sparse Bayesian factor modeling of transcriptional regulatory networks

Jia Meng, Jianqiu Zhang, Yidong Chen, Yufei Huang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM) coupled with its ICM solution is proposed. BSCRFM models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF regulated genes and it admits prior knowledge from existing database regarding TF regulated target genes. An efficient Iterated Conditional Modes (ICM) algorithm is developed, and a maximum a posterior (MAP) solution is calculated from multiple ICM results to avoid the local maximum problem, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can then be obtained. The proposed model's ICM algorithm and MAP solution are evaluated on the simulated systems and results demonstrated the validity and effectiveness of the proposed approach. The proposed model is also applied to the breast cancer microarray data and a TF regulated network is obtained.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
    Pages335-340
    Number of pages6
    DOIs
    StatePublished - 2010
    Event2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 - Hong Kong, China
    Duration: Dec 18 2010Dec 21 2010

    Publication series

    NameProceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010

    Other

    Other2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
    Country/TerritoryChina
    CityHong Kong
    Period12/18/1012/21/10

    ASJC Scopus subject areas

    • Biomedical Engineering
    • Health Informatics

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