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Microbiome Data Mining for Microbial Interactions and Relationships

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Big Data Analytics

Abstract

The study of how microbial species coexist and interact in a host-associated environment or a natural environment is crucial to advance basic microbiology science and the understanding of human health and diseases. Researchers have started to infer common interspecies interactions and species–phenotype relations such as competitive and cooperative interactions leveraging to big microbiome data. These endeavors have facilitated the discovery of previously unknown principles of microbial world and expedited the understanding of the disease mechanism. In this review, we will summarize current computational efforts in microbiome data mining for discovering microbial interactions and relationships including dimension reduction and data visualization, association analysis, microbial network reconstruction, as well as dynamic modeling and simulations.

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Acknowledgments

This work was supported in part by NSF IIP 1160960, NNS IIP 1332024, NSFC 61532008, and China National 12-5 plan 2012BAK24B01 and the international cooperation project of Hubei Province (No. 2014BHE0017) and the Self-determined Research Funds of CCNU from the Colleges’ Basic Research and Operation of MOE (No. CCNU16KFY04).

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Authors and Affiliations

  1. School of Computer, Central China Normal University, Wuhan, 430079, Hubei, China

    Xingpeng Jiang & Xiaohua Hu

  2. College of Computing and Informatics, Drexel University, Philadelphia, PA, 19104, USA

    Xiaohua Hu

Authors
  1. Xingpeng Jiang
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  2. Xiaohua Hu
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Correspondence to Xiaohua Hu .

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Editors and Affiliations

  1. Indian Institute of Public Health , Hyderabad, India

    Saumyadipta Pyne

  2. CRRao AIMSCS, University of Hyderabad Campus CRRao AIMSCS, Hyderabad, India

    B.L.S. Prakasa Rao

  3. CRRao AIMSCS, University of Hyderabad Campus CRRao AIMSCS, Hyderabad, India

    S.B. Rao

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Jiang, X., Hu, X. (2016). Microbiome Data Mining for Microbial Interactions and Relationships. In: Pyne, S., Rao, B., Rao, S. (eds) Big Data Analytics. Springer, New Delhi. https://doi.org/10.1007/978-81-322-3628-3_12

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