ArticlePLoS computational biology2020
A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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
19 citing papers in PubMed.
- Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types.Gut microbes · 2026Article
- Early-life microbiome trajectories as biomarkers to predict health outcomes.Microbiome research reports · 2026Review
- Specification curve analysis of the TEDDY study reveals large variation in microbiome-based T1D predictive performance.Nature communications · 2025Article
- Multitask knowledge-primed neural network for predicting missing metadata and host phenotype based on human microbiome.Bioinformatics advances · 2025Article
- Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts.mSystems · 2023Article
- Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning.International journal of molecular sciences · 2023Article
- Article
- Article
- Article
- From the nose to the lungs: the intricate journey of airborne pathogens amid commensal bacteria.American journal of physiology. Cell physiology · 2022Review
- Locality-Sensitive Hashing-Based k-Mer Clustering for Identification of Differential Microbial Markers Related to Host Phenotype.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images.Nature communications · 2022Article
- Systematically assessing microbiome-disease associations identifies drivers of inconsistency in metagenomic research.PLoS biology · 2022Article
- Delivery mode and perinatal antibiotics influence the predicted metabolic pathways of the gut microbiome.Scientific reports · 2021Article
- Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.Genome biology · 2021Article
- Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.Frontiers in microbiology · 2021Review
- Article
- Human gut microbiome aging clocks based on taxonomic and functional signatures through multi-view learning.Gut microbesArticle
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The microbiome is a new frontier for building predictors of human phenotypes. However, machine learning in the microbiome is fraught with issues of reproducibility, driven in large part by the wide range of analytic models and metagenomic data types available. We aimed to build robust metagenomic predictors of host phenotype by comparing prediction performances and biological interpretation across 8 machine learning methods and 4 different types of metagenomic data. Using 1,570 samples from 300 infants, we fit 7,865 models for 6 host phenotypes. We demonstrate the dependence of accuracy on algorithm choice and feature definition in microbiome data and propose a framework for building microbiome-derived indicators of host phenotype. We additionally identify biological features predictive of age, sex, breastfeeding status, historical antibiotic usage, country of origin, and delivery type. Our complete results can be viewed at http://apps.chiragjpgroup.org/ubiome_predictions/.
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.