Machine learning prediction of obesity-associated gut microbiota: identifying Bifidobacterium pseudocatenulatum as a potential therapeutic target
BackgroundThe rising prevalence of obesity and related metabolic disorders highlights the urgent need for innovative research approaches. Utilizing machine learning (ML) algorithms to predict obesity-associated gut microbiota and validating their efficacy with specific bacterial strains could signif...
Saved in:
Main Authors: | Hao Wu, Yuan Li, Yuxuan Jiang, Xinran Li, Shenglan Wang, Changle Zhao, Ximiao Yang, Baocheng Chang, Juhong Yang, Jianjun Qiao |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2025-02-01
|
Series: | Frontiers in Microbiology |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2024.1488656/full |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Bifidobacterium fermentation with infant formulas is associated with benefits for gut and brain barrier function
by: Emily G. Knox, et al.
Published: (2025-02-01) -
XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites
by: Salman Khan, et al.
Published: (2025-02-01) -
Prebiotic property of tamarind seed kernel on Bifidobacterium animalis growth and biofilm formation
by: Roongrawee Wandee, et al.
Published: (2025-01-01) -
Profiling the response of individual gut microbes to free fatty acids (FFAs) found in human milk
by: Megan E. Waller, et al.
Published: (2025-02-01) -
An ultrasonic-AI hybrid approach for predicting void defects in concrete-filled steel tubes via enhanced XGBoost with Bayesian optimization
by: Shuai Wan, et al.
Published: (2025-07-01)