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Revisit of Machine Learning Supported Biological and Biomedical Studies

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Book cover Computational Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1754))

Abstract

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

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  1. Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy Science, Shanghai, China

    Xiang-tian Yu, Lu Wang & Tao Zeng

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  1. Xiang-tian Yu
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  2. Lu Wang
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  1. Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Tao Huang

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Yu, Xt., Wang, L., Zeng, T. (2018). Revisit of Machine Learning Supported Biological and Biomedical Studies. In: Huang, T. (eds) Computational Systems Biology. Methods in Molecular Biology, vol 1754. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7717-8_11

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  • DOI: https://doi.org/10.1007/978-1-4939-7717-8_11

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