ArticleBMC oral health2025
Ensemble learning for microbiome-based caries diagnosis: multi-group modeling and biological interpretation from salivary and plaque metagenomic data.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOral microbiota is a major etiological factor in the development of dental caries. Next-generation sequencing techniques have been widely used, generating vast amounts of data which is underexplored. The advancement of artificial intelligence (AI) technologies has made it possible to mine information from these large datasets. This study aimed to develop AI-driven diagnostic models and identify key microbial features for caries.
methodsWe collected raw metagenomic and full-length 16 S rRNA gene sequencing data from previous studies on saliva and plaque to construct a caries AI training dataset comprising nearly 600 samples. Samples were grouped based on age, sequencing and sampling method. Through systematic comparison of seven machine learning architectures, including Logistic Regression, Random Forest, Support Vector Machines, Gradient Boosting, Convolutional Neural Networks, Feedforward Neural Networks, and Transformer models, we developed subgroup-specific caries diagnostic models, with subsequent ensemble learning integration to enhance generalizability.
resultsThe caries diagnostic model achieved a maximum AUC value of 1 (accuracy of 100%) for children under 6 years old in both saliva and plaque groups. The consistency of top features (species and metabolic pathways) contributing to the models was demonstrated through intra- and inter-group analyses. Key caries-associated species included Streptococcus salivarius, Streptococcus parasanguinis and Veillonella dispar. Veillonella parvula exhibits higher abundance in caries plaque samples, while being elevated in healthy saliva samples. Metabolic pathways like geranylgeranyl diphosphate and fructan biosynthesis were enriched in caries, whereas Bifidobacterium shunt and peptidoglycan biosynthesis were depleted.
conclusionThe current work provided reliable diagnostic models for early childhood caries, and established a robust computational framework for AI-driven microbiome analysis. This study, by focusing on the characteristics of the oral microbiome, offers novel perspectives for data mining and validation of existing data through the application of AI modelling.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.