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

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, metatransc...