ArticleMicrobiome2025
Effects of data transformation and model selection on feature importance in microbiome classification data.
Article in Microbiome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Deciphering Spatiotemporal Dynamics of Fermented Grains in the Jiangxiangxing Baijiu Production Process: Insights From a Transformer-Based Deep Learning Model.Journal of food science · 2026Article
- Gut microbiome signatures associate with DNA methylation-based biological aging.Scientific reports · 2026Article
- Prevalence aware feature selection improves biomarker identification in microbiome studies.Bioinformatics (Oxford, England) · 2026Article
- From microbes to milestones: Gut bacterial abundances and functional pathways associate with neurodevelopment following preterm birth.Gut microbiology · 2026Article
- A systematic review of artificial intelligence and machine learning for gut microbiome-based CRC screening.Journal of gastrointestinal oncology · 2026Review
- Transformer Models, Graph Networks, and Generative AI in Gut Microbiome Research: A Narrative Review.Bioengineering (Basel, Switzerland) · 2026Review
- Persistent effects of dietary selection and inbreeding on microbiome composition and longevity in Drosophila.BMC ecology and evolution · 2026Article
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- Predicting maize hybrid performance with machine learning and a locus-specific weighted degree of dominance transformation.Frontiers in plant science · 2026Article
- Stool microbiota variations along the adenoma-colorectal carcinoma sequence - robustness of disease-associated microbial features.Frontiers in microbiology · 2026Article
- Decoding the reproductive microbiome: enabling clinical and biological insights through machine and deep learning.Frontiers in endocrinology · 2026Review
- Artificial intelligence empowers gut microbiota research in neurodegenerative diseases molecular mechanisms and precision therapy.iScience · 2025Review
- Predicting allergy and postpartum depression from an incomplete compositional microbiome.BMC genomics · 2025Article
- Oral and fecal microbiota in Chinese adults with obesity reveal potential niche-specific microbiota associated with obesity.BMC microbiology · 2025Article
- Identifying Optimal Machine Learning Approaches for Human Gut Microbiome (Shotgun Metagenomics) and Metabolomics Integration with Stable Feature Selection.bioRxiv : the preprint server for biology · 2025Article
- Computational Metagenomics: State of the Art.International journal of molecular sciences · 2025Review
- The Estonian Biobank's journey from biobanking to personalized medicine.Nature communications · 2025Review
- Elementary methods provide more replicable results in microbial differential abundance analysis.Briefings in bioinformatics · 2025Article
- Exploring the role of normalization and feature selection in microbiome disease classification pipelines.GigaScience · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate classification of host phenotypes from microbiome data is crucial for advancing microbiome-based therapies, with machine learning offering effective solutions. However, the complexity of the gut microbiome, data sparsity, compositionality, and population-specificity present significant challenges. Microbiome data transformations can alleviate some of the aforementioned challenges, but their usage in machine learning tasks has largely been unexplored.
resultsOur analysis of over 8500 samples from 24 shotgun metagenomic datasets showed that it is possible to classify healthy and diseased individuals using microbiome data with minimal dependence on the choice of algorithm or transformation. Presence-absence transformations performed comparably to abundance-based transformations, and only a small subset of predictors is necessary for accurate classification. However, while different transformations resulted in comparable classification performance, the most important features varied significantly, which highlights the need to reevaluate machine learning-based biomarker detection.
conclusionsMicrobiome data transformations can significantly influence feature selection but have a limited effect on classification accuracy. Our findings suggest that while classification is robust across different transformations, the variation in feature selection necessitates caution when using machine learning for biomarker identification. This research provides valuable insights for applying machine learning to microbiome data and identifies important directions for future work.
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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.