ReviewFrontiers in genetics2021
RFtest: A Robust and Flexible Community-Level Test for Microbiome Data Powerfully Detects Phylogenetically Clustered Signals.
Review in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Leveraging artificial intelligence for analysis of the gut microbiome for dementia diagnosis: a scoping review and discussion.Frontiers in dementia · 2026Pooled it
- Ensemble test for microbiome data.Microbiome · 2026Article
- Compiling an early life human gut microbiome atlas and identification of key microbial drivers.NPJ biofilms and microbiomes · 2025Article
- BMDD: A probabilistic framework for accurate imputation of zero-inflated microbiome sequencing data.PLoS computational biology · 2025Article
- 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
- Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease.Journal of translational medicine · 2025Article
- New bitongling regulates gut microbiota to predict angiogenesis in rheumatoid arthritis via the gut-joint axis: a deep neural network approach.Frontiers in microbiology · 2025Article
- Bipolar disorder: Construction and analysis of a joint diagnostic model using random forest and feedforward neural networks.IBRO neuroscience reports · 2024Article
- Human Gut Microbiota for Diagnosis and Treatment of Depression.International journal of molecular sciences · 2024Review
- Interpretable metric learning in comparative metagenomics: The adaptive Haar-like distance.PLoS computational biology · 2024Article
- Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning.International journal of molecular sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Random forest is considered as one of the most successful machine learning algorithms, which has been widely used to construct microbiome-based predictive models. However, its use as a statistical testing method has not been explored. In this study, we propose "Random Forest Test" (RFtest), a global (community-level) test based on random forest for high-dimensional and phylogenetically structured microbiome data. RFtest is a permutation test using the generalization error of random forest as the test statistic. Our simulations demonstrate that RFtest has controlled type I error rates, that its power is superior to competing methods for phylogenetically clustered signals, and that it is robust to outliers and adaptive to interaction effects and non-linear associations. Finally, we apply RFtest to two real microbiome datasets to ascertain whether microbial communities are associated or not with the outcome variables.
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.