ArticleCell reports methods2023
MaLiAmPi enables generalizable and taxonomy-independent microbiome features from technically diverse 16S-based microbiome studies.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Decoding the reproductive microbiome: enabling clinical and biological insights through machine and deep learning.Frontiers in endocrinology · 2026Review
- Computational Metagenomics: State of the Art.International journal of molecular sciences · 2025Review
- Breast milk IgG engages the mouse neonatal immune system to instruct responses to gut antigens.Science (New York, N.Y.) · 2025Article
- Inflammation-Induced Th17 Cells Synergize with the Inflammation-Trained Microbiota to Mediate Host Resiliency Against Intestinal Injury.Inflammatory bowel diseases · 2025Article
- Nasal microbiota predictors for methicillin resistant Staphylococcus colonization in critically ill children.PloS one · 2025Article
- Discovery of sparse, reliable omic biomarkers with Stabl.Nature biotechnology · 2024Article
- VMAP: Vaginal Microbiome Atlas during Pregnancy.JAMIA open · 2024Article
- Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.Cell reports. Medicine · 2024Article
- Data-driven insights can transform women's reproductive health.npj women's health · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
For studies using microbiome data, the ability to robustly combine data from technically and biologically distinct microbiome studies is a crucial means of supporting more robust and clinically relevant inferences. Formidable technical challenges arise when attempting to combine data from technically diverse 16S rRNA gene variable region amplicon sequencing (16S) studies. Closed operational taxonomic units and taxonomy are criticized as being heavily dependent upon reference sets and with limited precision relative to the underlying biology. Phylogenetic placement has been demonstrated to be a promising taxonomy-free manner of harmonizing microbiome data, but it has lacked a validated count-based feature suitable for use in machine learning and association studies. Here we introduce a phylogenetic-placement-based, taxonomy-independent, compositional feature of microbiota: phylotypes. Phylotypes were predictive of clinical outcomes such as obesity or pre-term birth on technically diverse independent validation sets harmonized post hoc. Thus, phylotypes enable the rigorous cross-validation of 16S-based clinical prognostic models and associative microbiome studies.
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Registered trials
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