Gut-host Crosstalk: Methodological and Computational Challenges.
Data integration
Gut-host crosstalk
Predictive computational modeling
Journal
Digestive diseases and sciences
ISSN: 1573-2568
Titre abrégé: Dig Dis Sci
Pays: United States
ID NLM: 7902782
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
pubmed:
6
2
2020
medline:
29
7
2020
entrez:
5
2
2020
Statut:
ppublish
Résumé
Understanding how health-promoting microbiota are established and their beneficial interactions with the host is of critical biomedical importance. The current high throughput data acquisition technologies allow for integrating components of the gut ecosystem. The richness of data types and large number of measured variables involved underscores the critical importance of the appropriate choice of analytical and computational methods that can be used to model this complex ecosystem. This review outlines currently used approaches to perform analyses of data obtained as a result of interrogation of the gut-microbiota ecosystem and the challenges associated with these methodological and computational efforts. The problem of large dimensionality versus small numbers of samples is explained with discussions of clustering, dimensionality reduction, and statistical testing. Predictive modeling and data integration specific to the gut ecosystem are also discussed.
Identifiants
pubmed: 32016821
doi: 10.1007/s10620-020-06105-9
pii: 10.1007/s10620-020-06105-9
pmc: PMC7176510
mid: NIHMS1555977
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
686-694Subventions
Organisme : NIDDK NIH HHS
ID : R01 DK107561
Pays : United States
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