Microbiome Data Mining for Microbial Interactions and Relationships

Xingpeng Jiang () and Xiaohua Hu ()
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Xingpeng Jiang: Central China Normal University, School of Computer
Xiaohua Hu: Central China Normal University, School of Computer

A chapter in Big Data Analytics, 2016, pp 221-235 from Springer

Abstract: 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.

Keywords: Microbial Community; Canonical Correlation Analysis; Flux Balance Analysis; Nonnegative Matrix Factorization; Microbial Interaction (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-81-322-3628-3_12

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DOI: 10.1007/978-81-322-3628-3_12

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