Machine learning for data integration in human gut microbiome

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...

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Published inMicrobial cell factories Vol. 21; no. 1; pp. 1 - 16
Main Authors Li, Peishun, Luo, Hao, Ji, Boyang, Nielsen, Jens
Format Journal Article
LanguageEnglish
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
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Abstract 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.
AbstractList Abstract 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.
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.
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. Keywords: Gut microbiome, Data integration, Machine learning, Precision medicine, Multi-omics
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.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.
ArticleNumber 241
Audience Academic
Author Nielsen, Jens
Ji, Boyang
Luo, Hao
Li, Peishun
Author_xml – sequence: 1
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  surname: Ji
  fullname: Ji, Boyang
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  givenname: Jens
  surname: Nielsen
  fullname: Nielsen, Jens
BackLink https://research.chalmers.se/publication/533431$$DView record from Swedish Publication Index
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Snippet Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to...
Abstract Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely...
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SubjectTerms 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
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Title Machine learning for data integration in human gut microbiome
URI https://www.proquest.com/docview/2755774632
https://www.proquest.com/docview/2739741848
https://pubmed.ncbi.nlm.nih.gov/PMC9685977
https://research.chalmers.se/publication/533431
https://doaj.org/article/0d9479207eb141d990683738fdf11e9c
Volume 21
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