Neonatal gut microbiota stratification and identification of SCFA-associated microbial subgroups using unsupervised clustering and machine learning classification
Unsupervised clustering combined with classification analysis effectively predict SCFAs-associated subgroups and paving the way for future research on longitudinal tracking and functional genomic integration in early-life metabolic health.
Authors
- Kee Hyun Cho (ORCID: https://orcid.org/0000-0002-6358-5734)
- Cheol‐Heui Yun (ORCID: https://orcid.org/0000-0002-0041-2887)
- Su Jin Jeong
- Payam Hosseinzadeh Kasani
Institutions
- Seoul National University (KR)
- Biocon (Switzerland) (CH)
- Kangwon National University Hospital (KR)
- CHA University Bundang Medical Center (KR)
- CHA University (KR)
Publication Details
- Journal
- Frontiers in Microbiology
- Published
- 2025-12-04
- DOI
- https://doi.org/10.3389/fmicb.2025.1668451
- Primary Topic
- Gut microbiota and health
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Kangwon National University
- Korea Health Industry Development Institute