Using knowledge driven matrix factorization to reconstruct modular gene regulatory network

Yang Zhou, Zheng Li, Xuerui Yang, Linxia Zhang, Shireesh Srivastava, Rong Jin, Christina Chan

    Research output: ResearchConference contribution

    Abstract

    Reconstructing gene networks from micro-array data can provide information on the mechanisms that govern cellular processes. Numerous studies have been devoted to addressing this problem. A popular method is to view the gene network as a Bayesian inference network, and to apply structure learning methods to determine the topology of the gene network. There are, however, several shortcomings with the Bayesian structure learning approach for reconstructing gene networks. They include high computational cost associated with analyzing a large number of genes and inefficiency in exploiting prior knowledge of co-regulation that could be derived from Gene Ontology (GO) information. In this paper, we present a knowledge driven matrix factorization (KMF) framework for reconstructing modular gene networks that addresses these shortcomings. In KMF, gene expression data is initially used to estimate the correlation matrix. The gene modules and the interactions among the modules are derived by factorizing the correlation matrix. The prior knowledge in GO is integrated into matrix factorization to help identify the gene modules. An alternating optimization algorithm is presented to efficiently find the solution. Experiments show that our algorithm performs significantly better in identifying gene modules than several state-of-the-art algorithms, and the interactions among the modules uncovered by our algorithm are proved to be biologically meaningful.

    LanguageEnglish (US)
    Title of host publicationProceedings of the National Conference on Artificial Intelligence
    Pages811-816
    Number of pages6
    Volume2
    StatePublished - 2008
    Event23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08 - Chicago, IL, United States
    Duration: Jul 13 2008Jul 17 2008

    Other

    Other23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08
    CountryUnited States
    CityChicago, IL
    Period7/13/087/17/08

    Profile

    Factorization
    Genes
    Ontology
    Gene expression
    Topology
    Costs
    Experiments

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

    Cite this

    Zhou, Y., Li, Z., Yang, X., Zhang, L., Srivastava, S., Jin, R., & Chan, C. (2008). Using knowledge driven matrix factorization to reconstruct modular gene regulatory network. In Proceedings of the National Conference on Artificial Intelligence (Vol. 2, pp. 811-816)

    Using knowledge driven matrix factorization to reconstruct modular gene regulatory network. / Zhou, Yang; Li, Zheng; Yang, Xuerui; Zhang, Linxia; Srivastava, Shireesh; Jin, Rong; Chan, Christina.

    Proceedings of the National Conference on Artificial Intelligence. Vol. 2 2008. p. 811-816.

    Research output: ResearchConference contribution

    Zhou, Y, Li, Z, Yang, X, Zhang, L, Srivastava, S, Jin, R & Chan, C 2008, Using knowledge driven matrix factorization to reconstruct modular gene regulatory network. in Proceedings of the National Conference on Artificial Intelligence. vol. 2, pp. 811-816, 23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08, Chicago, IL, United States, 7/13/08.
    Zhou Y, Li Z, Yang X, Zhang L, Srivastava S, Jin R et al. Using knowledge driven matrix factorization to reconstruct modular gene regulatory network. In Proceedings of the National Conference on Artificial Intelligence. Vol. 2. 2008. p. 811-816.
    Zhou, Yang ; Li, Zheng ; Yang, Xuerui ; Zhang, Linxia ; Srivastava, Shireesh ; Jin, Rong ; Chan, Christina. / Using knowledge driven matrix factorization to reconstruct modular gene regulatory network. Proceedings of the National Conference on Artificial Intelligence. Vol. 2 2008. pp. 811-816
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