ArticlemBio2020
A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems.
Article in mBio, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 146 papers, 7 of them syntheses that pooled it.
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
146 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Artificial intelligence for genomic science: a scoping review of concepts, architectures, applications, and open challenges.Frontiers in bioinformatics · 2026Pooled it
- [Advances in the Application of Forensic Microbiome Analysis in Tissue Source Inference].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2025Pooled it
- Bibliometric analysis of the application of artificial intelligence techniques in bacteriology: a PRISMA-guided research agenda.Frontiers in microbiology · 2025Pooled it
- Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth.BMC biology · 2023Pooled it
- Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions.Microbiome · 2023Pooled it
- Meta-analysis defines predominant shared microbial responses in various diseases and a specific inflammatory bowel disease signal.Genome biology · 2022Pooled it
- Meta-Analysis of Bovine Digital Dermatitis Microbiota Reveals Distinct Microbial Community Structures Associated With Lesions.Frontiers in cellular and infection microbiology · 2021Pooled it
- Gut microbiome in type 2 diabetes: insights from metagenomics, multi-omics, and diet-microbe interactions.Gut microbes · 2026Review
- Dynamic flexibility of the murine gut microbiota during morphine disturbance enables escape from the stable dysbiosis that is associated with addiction-like behavior.Gut microbes · 2026Article
- Short-chain fatty acid-producing commensals: Key modulators of AD neuroinflammation via the microbiota-gut-brain axis.Current research in microbial sciences · 2026Review
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Microbial Signals in Cancer: Dissecting Host-Microbiota-Tumor Interactions and Potential Therapeutic Strategy.MedComm · 2026Review
- Inclusion of Multi-Omic Biomarkers Improves Prediction Accuracy of Response, Relapse, and Overall Survival in Acute Myeloid Leukemia Patients Receiving High-Intensity Induction Chemotherapy.Cancer medicine · 2026Article
- Foundation Models for Microbiome Research: From Sequence Semantics to Community Dynamics and Multimodal World Models.Advanced genetics (Hoboken, N.J.) · 2026Review
- An Interpretable Machine Learning Approach to Ecologically Characterize Soil Carbon and Structure From Multi-Kingdom Microbiome, Texture and Climate.Molecular ecology · 2026Article
- Intratumoral microbiota in colorectal cancer: roles, therapeutic potential, and challenges.Journal of advanced research · 2026Review
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- Gut Microbiome Dysbiosis in Atopic Dermatitis: Pathogenic Mechanisms, Gut-Skin Axis Disruption, and Emerging Microbiota-Targeted Therapies.Biomedicines · 2026Review
- Advances and future directions in identifying specific taxa from microbial meta-omics data: from pipeline to deep learning.mSystems · 2026Review
- Identifying gut microbiome signatures of type 1 diabetes using machine learning and evolutionary feature selection.BMC microbiology · 2026Article
86 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs) (
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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.