Research progress of precision nutrition promoted by the combination of machine learning and multi-omics

doi:10.3877/cma.j.issn.1673-9450.2022.06.014

Abstract

Abstract:

With the development of clinical big data, precision nutrition has gradually become an important trend in clinical nutrition research. Precision nutrition integrates the research theories and methods of genomics, metabolomics, phenoomics and other omics, and introduces computational science and technology such as machine learning, which makes people′s understanding of nutritional metabolic disorders under various diseases/pathophysiological conditions reach a new height, and also provides a new paradigm for the discovery of new nutritional intervention targets and intervention modes. This paper reviews the data preparation and algorithm selection methods of machine learning model building in precision nutrition, combined with the application of machine learning in multi-omics research. Under the background of big data, precision nutrition research with the application of machine learning can not only provide scientific basis for the formulation of nutrition support plan for each patient, but also explore the relationship and even causal relationship between various nutrients. Therefore, machine learning is an essential technical means in precision nutrition research. However, this paper also points out that the current application of machine learning in precision nutrition is limited by high heterogeneity of data and low specificity of algorithms, and there are still many unknown areas to explore.

Key words: Nutrigenomics, Precision medicine, Machine learning, Deep learning, Metabolomics

Lu Wang, Yu Wang, Jun Zeng, Wei Chen, Hua Jiang. Research progress of precision nutrition promoted by the combination of machine learning and multi-omics[J]. Chinese Journal of Injury Repair and Wound Healing(Electronic Edition), 2022, 17(06): 540-544.

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