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Integrating Multiple Types of Data to Identify MicroRNA–Gene Co-modules

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Ovarian Cancer

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1049))

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Abstract

MicroRNAs (miRNAs) and genes work cooperatively to form the kernel part of gene regulatory system and affect many crucial biological processes. However, the detailed combinatorial roles of most miRNAs and genes in cellular processes and diseases are still unclear. The huge amount of diverse functional genomic data provides unprecedented opportunities to study the miRNA–gene co-regulations. How to integrate diverse genomic data to identify the regulatory modules of miRNAs and genes is a challenging problem in computational biology. Recently, we have proposed a mathematical data integration framework to discover the miRNA–gene regulatory co-modules. We have applied the proposed method to integrate a set of heterogeneous data sources including the expression profiles of miRNAs and genes on 385 human ovarian cancer samples as well as miRNA–gene interactions and gene–gene interactions. The revealed co-modules show significant biological relevance and potential associations with ovarian cancers and others.

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Acknowledgements

This project was supported by the National Natural Science Foundation of China (No.11001256), the ‘Special Presidential Prize—Scientific Research Foundation of the CAS, and the Special Foundation of President of AMSS at CAS for ‘Chen Jing-Run’ Future Star Program.

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Authors and Affiliations

  1. Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Science, Beijing, China

    Shihua Zhang

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  1. Shihua Zhang
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Editors and Affiliations

  1. Group of Experimental Therapeutics, Federal State Institution N.N.Petrov Res Institute of Oncology, St. Petersburg, Russia

    Anastasia Malek

  2. Labs of Molecular Tumor Pathology & Func, Charité - Universitatsmedizin Berlin, Berlin, Germany

    Oleg Tchernitsa

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Zhang, S. (2013). Integrating Multiple Types of Data to Identify MicroRNA–Gene Co-modules. In: Malek, A., Tchernitsa, O. (eds) Ovarian Cancer. Methods in Molecular Biology, vol 1049. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-547-7_16

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  • DOI: https://doi.org/10.1007/978-1-62703-547-7_16

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-546-0

  • Online ISBN: 978-1-62703-547-7

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