Revisit of Machine Learning Supported Biological and Biomedical Studies

  • Xiang-tian Yu
  • Lu Wang
  • Tao Zeng
Part of the Methods in Molecular Biology book series (MIMB, volume 1754)


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.

Key words

Machine learning Feature selection Clustering Classification Omics big data Association Causality Gut metagenomics Precision medicine 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Systems Biology, Institute of Biochemistry and Cell BiologyChinese Academy ScienceShanghaiChina

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