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Microbiome Data Mining for Microbial Interactions and Relationships

In: Big Data Analytics

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  • Xingpeng Jiang

    (Central China Normal University, School of Computer)

  • Xiaohua Hu

    (Central China Normal University, School of Computer
    Drexel University, College of Computing and Informatics)

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.

Suggested Citation

  • Xingpeng Jiang & Xiaohua Hu, 2016. "Microbiome Data Mining for Microbial Interactions and Relationships," Springer Books, in: Saumyadipta Pyne & B.L.S. Prakasa Rao & S.B. Rao (ed.), Big Data Analytics, pages 221-235, Springer.
    • Handle: RePEc:spr:sprchp:978-81-322-3628-3_12
    DOI: 10.1007/978-81-322-3628-3_12
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