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Artificial intelligence and metagenomics in intestinal diseases
Author(s) -
Lin Yufeng,
Wang Guoping,
Yu Jun,
Sung Joseph J Y
Publication year - 2021
Publication title -
journal of gastroenterology and hepatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.214
H-Index - 130
eISSN - 1440-1746
pISSN - 0815-9319
DOI - 10.1111/jgh.15501
Subject(s) - metagenomics , microbiome , artificial intelligence , machine learning , medicine , gut flora , disease , computational biology , computer science , bioinformatics , data science , biology , pathology , immunology , biochemistry , gene
Gut microbiota has been shown to associate with the development of gastrointestinal diseases. In the last decade, development in whole metagenome sequencing and 16S rRNA sequencing technology has dramatically accelerated the gut microbiome's research and revealed its association with gastrointestinal disorders. Because of high dimensionality and complexity's intrinsic data characteristics, traditional bioinformatical methods could only explain the most significant changes with limited prediction accuracy. In contrast, machine learning is the application of artificial intelligence that provides the computational systems to automatically learn and improve from experience (training cohort) without being explicitly programmed. It is thus capable of unwiring high dimensionality and complicated correlational hitches. With modern computation power, machine learning is widely utilized to analyze microorganisms related to disease onset and other clinical features. It could help explore and identify novel biomarkers or improve the accuracy rate of disease diagnostic. This review summarized the most recent research that utilized machine learning to reveal the role of gut microbiota in intestinal disorders.