Machine learning for data integration in human gut microbiome

• Published in: Microbial cell factories Vol. 21; no. 1; pp. 1 - 16
• Main Authors: Li, Peishun, Luo, Hao, Ji, Boyang, Nielsen, Jens
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 23.11.2022; BioMed Central; BMC
• Subjects: Algorithms; Artificial intelligence; Autism; Biomarkers; Complications and side effects; Data analysis; Data integration; Data mining; Deep learning; Development and progression; Diabetes; Digestive system; Disease; Dysbacteriosis; Dysbiosis; E coli; Enzymes; Gastrointestinal tract; Genes; Genomes; Gut microbiome; Gut microbiota; Inflammatory bowel disease; Insulin resistance; Intestinal microflora; Intestines; Learning algorithms; Liver; Machine learning; Medical research; Medical treatment; Medicine, Experimental; Metabolism; Metabolites; Metabolomics; Metagenomics; Methods; Microbiology; Microbiomes; Microbiota; Microbiota (Symbiotic organisms); Microorganisms; Multi-omics; Obesity; Phenotypes; Precision medicine; Probiotics; Review; Synthetic biology; Sweden
• ISSN: 1475-2859; 1475-2859
• DOI: 10.1186/s12934-022-01973-4

Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to characterize the microbial ecosystems, which led to an explosion of different types of molecular profiling data, such as metagenomics, metatranscriptomics and metabolomics. For analysis of such data, machine learning algorithms have shown to be useful for identifying key molecular signatures, discovering potential patient stratifications, and particularly for generating models that can accurately predict phenotypes. In this review, we first discuss how dysbiosis of the intestinal microbiota is linked to human disease development and how potential modulation strategies of the gut microbial ecosystem can be used for disease treatment. In addition, we introduce categories and workflows of different machine learning approaches, and how they can be used to perform integrative analysis of multi-omics data. Finally, we review advances of machine learning in gut microbiome applications and discuss related challenges. Based on this we conclude that machine learning is very well suited for analysis of gut microbiome and that these approaches can be useful for development of gut microbe-targeted therapies, which ultimately can help in achieving personalized and precision medicine.