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Identifying functional miRNA-mRNA regulatory modules with correspondence latent dirichlet allocation

  1. Author:
    Liu, B.
    Liu, L.
    Tsykin, A.
    Goodall, G. J.
    Green, J. E.
    Zhu, M.
    Kim, C. H.
    Li, J. Y.

  2. Author Address

    [Liu, Bing; Liu, Lin; Li, Jiuyong] Univ S Australia, Sch Comp & Informat Sci, Mawson Lakes, SA 5095, Australia. [Liu, Bing; Tsykin, Anna; Goodall, Gregory J.] SA Pathol, Ctr Canc Biol, Adelaide, SA 5000, Australia. [Tsykin, Anna] Univ Adelaide, Sch Mol & Biomed Sci, Adelaide, SA 5005, Australia. [Goodall, Gregory J.] Univ Adelaide, Dept Med, Adelaide, SA 5005, Australia. [Green, Jeffrey E.; Zhu, Min] NCI, Lab Canc Biol & Genet, Bethesda, MD 20892 USA. [Kim, Chang Hee] NCI, Lab Mol Technol, FCRDC, Frederick, MD 21702 USA.;Li, JY, Univ S Australia, Sch Comp & Informat Sci, Mawson Lakes, SA 5095, Australia.
    1. Year: 2010
    2. Date: Dec
  2. Journal: Bioinformatics
    1. 26
    2. 24
    3. Pages: 3105-3111
  4. Type of Article: Article
  5. ISSN: 1367-4803
  1. Abstract:

    Motivation: MicroRNAs ( miRNAs) are small non-coding RNAs that cause mRNA degradation and translational inhibition. They are important regulators of development and cellular homeostasis through their control of diverse processes. Recently, great efforts have been made to elucidate their regulatory mechanism, but the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With more and more matched expression profiles of miRNAs and mRNAs having been made available, it is of great interest to utilize both expression profiles to discover the functional regulatory networks of miRNAs and their target mRNAs for potential biological processes that they may participate in. Results: We present a probabilistic graphical model to discover functional miRNA regulatory modules at potential biological levels by integrating heterogeneous datasets, including expression profiles of miRNAs and mRNAs, with or without the prior target binding information. We applied this model to a mouse mammary dataset. It effectively captured several biological process specific modules involving miRNAs and their target mRNAs. Furthermore, without using prior target binding information, the identified miRNAs and mRNAs in each module show a large proportion of overlap with predicted miRNA target relationships, suggesting that expression profiles are crucial for both target identification and discovery of regulatory modules.

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External Sources

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  2. DOI: 10.1093/bioinformatics/btq576
  3. View record in Web of Science®
  4. WOS: 000284947700014

Library Notes

  1. Fiscal Year: FY2010-2011
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