SynthesisPLoS computational biology2022
Host phenotype classification from human microbiome data is mainly driven by the presence of microbial taxa.
Synthesis in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Differential co-occurrence analysis: a method to extract ecological modules from clinical microbiome data.mSystems · 2026Article
- MaAsLin 3: refining and extending generalized multivariable linear models for meta-omic association discovery.Nature methods · 2026Article
- Human DNA levels in feces reflect gut inflammation and associate with presence of gut species in IBD patients across the age spectrum.Microbiome · 2026Article
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Decoding the reproductive microbiome: enabling clinical and biological insights through machine and deep learning.Frontiers in endocrinology · 2026Review
- Rumen Microbiota-Based Machine Learning Approach for Predicting Heat Stress and Identifying Associated Microbes.Microbial ecology · 2025Article
- SHAP-based binarization enhances metataxonomic machine learning with application to gut microbiota of inflammatory bowel disease.Scientific reports · 2025Article
- Evaluating changes in attractor sets under small network perturbations to infer reliable microbial interaction networks from abundance patterns.Bioinformatics (Oxford, England) · 2025Article
- Elementary methods provide more replicable results in microbial differential abundance analysis.Briefings in bioinformatics · 2025Article
- Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota.NPJ biofilms and microbiomes · 2025Article
- Exploring the role of normalization and feature selection in microbiome disease classification pipelines.GigaScience · 2025Article
- Effects of data transformation and model selection on feature importance in microbiome classification data.Microbiome · 2025Article
- Quantifying uncertainty in microbiome-based prediction using Gaussian processes with microbial community dissimilarities.Bioinformatics advances · 2025Article
- Leveraging human microbiomes for disease prediction and treatment.Trends in pharmacological sciences · 2025Article
- MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.Briefings in bioinformatics · 2024Article
- The Therapeutic Potential of the Specific Intestinal Microbiome (SIM) Diet on Metabolic Diseases.Biology · 2024Review
- Article
- MKMR: a multi-kernel machine regression model to predict health outcomes using human microbiome data.Briefings in bioinformatics · 2023Article
- microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach.Frontiers in microbiology · 2023Article
- Machine learning-based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease.GigaScience · 2022Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Machine learning-based classification approaches are widely used to predict host phenotypes from microbiome data. Classifiers are typically employed by considering operational taxonomic units or relative abundance profiles as input features. Such types of data are intrinsically sparse, which opens the opportunity to make predictions from the presence/absence rather than the relative abundance of microbial taxa. This also poses the question whether it is the presence rather than the abundance of particular taxa to be relevant for discrimination purposes, an aspect that has been so far overlooked in the literature. In this paper, we aim at filling this gap by performing a meta-analysis on 4,128 publicly available metagenomes associated with multiple case-control studies. At species-level taxonomic resolution, we show that it is the presence rather than the relative abundance of specific microbial taxa to be important when building classification models. Such findings are robust to the choice of the classifier and confirmed by statistical tests applied to identifying differentially abundant/present taxa. Results are further confirmed at coarser taxonomic resolutions and validated on 4,026 additional 16S rRNA samples coming from 30 public case-control studies.
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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.