ArticleMicroorganisms2020
Clustering on Human Microbiome Sequencing Data: A Distance-Based Unsupervised Learning Model.
Article in Microorganisms, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Maternal gut microbiota in the postpartum Period: A Systematic review.European journal of obstetrics, gynecology, and reproductive biology · 2023Pooled it
- A New Unsupervised Machine Learning Method for Metabarcoding Analysis Based on Selected Mock Community Data.Computational and structural biotechnology journal · 2026Article
- Systems biology and microbiome innovations for personalized diabetic retinopathy management.NPJ systems biology and applications · 2025Review
- Nanomedicine-Driven Modulation of the Gut-Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer's and Parkinson's Disease.International journal of molecular sciences · 2025Review
- Operationalizing Team Science at the Academic Cancer Center Network to Unveil the Structure and Function of the Gut Microbiome.Journal of clinical medicine · 2025Review
- Multidimensional scaling improves distance-based clustering for microbiome data.Bioinformatics (Oxford, England) · 2025Article
- Lippia sidoides essential oil at concentration of 0.25% provided improvements in microbiota and intestine integrity of Danio rerio.Brazilian journal of veterinary medicine · 2024Article
- The healthy equine uterus harbors a distinct core microbiome plus a rich and diverse microbiome that varies with geographical location.Scientific reports · 2022Article
- COVID-19 heterogeneity in islands chain environment.PloS one · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Modeling and analyzing human microbiome allows the assessment of the microbial community and its impacts on human health. Microbiome composition can be quantified using 16S rRNA technology into sequencing data, which are usually skewed and heavy-tailed with excess zeros. Clustering methods are useful in personalized medicine by identifying subgroups for patients stratification. However, there is currently a lack of standardized clustering method for the complex microbiome sequencing data. We propose a clustering algorithm with a specific beta diversity measure that can address the presence-absence bias encountered for sparse count data and effectively measure the sample distances for sample stratification. Our distance measure used for clustering is derived from a parametric based mixture model producing sample-specific distributions conditional on the observed operational taxonomic unit (OTU) counts and estimated mixture weights. The method can provide accurate estimates of the true zero proportions and thus construct a precise beta diversity measure. Extensive simulation studies have been conducted and suggest that the proposed method achieves substantial clustering improvement compared with some widely used distance measures when a large proportion of zeros is presented. The proposed algorithm was implemented to a human gut microbiome study on Parkinson's diseases to identify distinct microbiome states with biological interpretations.
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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.