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Machine learning framework for gut microbiome biomarkers discovery and modulation analysis in large-scale obese population
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Machine learning framework for gut microbiome biomarkers discovery and modulation analysis in large-scale obese population
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Journal Article
Machine learning framework for gut microbiome biomarkers discovery and modulation analysis in large-scale obese population
2022
Overview
Background
The gut microbiome has proven to be an important factor affecting obesity; however, it remains a challenge to identify consistent biomarkers across geographic locations and perform precisely targeted modulation for obese individuals.
Results
This study proposed a systematic machine learning framework and applied it to 870 human stool metagenomes across five countries to obtain comprehensive regional shared biomarkers and conduct a personalized modulation analysis. In our pipeline, a heterogeneous ensemble feature selection diagram is first developed to determine an optimal subset of biomarkers through the aggregation of multiple techniques. Subsequently, a deep reinforcement learning method was established to alter the targeted composition to the desired healthy target. In this manner, we can realize personalized modulation by counterfactual inference. Consequently, a total of 42 species were identified as regional shared biomarkers, and they showed good performance in distinguishing obese people from the healthy group (area under curve (AUC) =0.85) when demonstrated on validation datasets. In addition, by pooling all counterfactual explanations, we found that
Akkermansia muciniphila
,
Faecalibacterium prausnitzii, Prevotella copri, Bacteroides dorei, Bacteroides eggerthii, Alistipes finegoldii, Alistipes shahii, Eubacterium
sp.
_CAG_180,
and
Roseburia hominis
may be potential broad-spectrum targets with consistent modulation in the multi-regional obese population.
Conclusions
This article shows that based on our proposed machine-learning framework, we can obtain more comprehensive and accurate biomarkers and provide modulation analysis for the obese population. Moreover, our machine-learning framework will also be very useful for other researchers to further obtain biomarkers and perform counterfactual modulation analysis in different diseases.
Publisher
BioMed Central,BioMed Central Ltd,BMC
