ArticleFrontiers in microbiology2018
A Phylogeny-Regularized Sparse Regression Model for Predictive Modeling of Microbial Community Data.
Article in Frontiers in microbiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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Who cites it
12 citing papers in PubMed.
- A Tree-Based Model for Addressing Sparsity and Taxa Covariance in Microbiome Compositional Count Data.Statistics in medicine · 2026Article
- A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions.Microbiome · 2022Article
- Article
- LinDA: linear models for differential abundance analysis of microbiome compositional data.Genome biology · 2022Article
- Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture.Microbiome · 2022Article
- Associations between the gut microbiome and metabolome in early life.BMC microbiology · 2021Article
- Feature selection and causal analysis for microbiome studies in the presence of confounding using standardization.BMC bioinformatics · 2021Article
- A novel deep learning method for predictive modeling of microbiome data.Briefings in bioinformatics · 2021Article
- RFtest: A Robust and Flexible Community-Level Test for Microbiome Data Powerfully Detects Phylogenetically Clustered Signals.Frontiers in genetics · 2021Review
- Incorporating biological structure into machine learning models in biomedicine.Current opinion in biotechnology · 2020Review
- A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.Frontiers in genetics · 2019Review
- Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy.Gut microbesArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Fueled by technological advancement, there has been a surge of human microbiome studies surveying the microbial communities associated with the human body and their links with health and disease. As a complement to the human genome, the human microbiome holds great potential for precision medicine. Efficient predictive models based on microbiome data could be potentially used in various clinical applications such as disease diagnosis, patient stratification and drug response prediction. One important characteristic of the microbial community data is the phylogenetic tree that relates all the microbial taxa based on their evolutionary history. The phylogenetic tree is an informative prior for more efficient prediction since the microbial community changes are usually not randomly distributed on the tree but tend to occur in clades at varying phylogenetic depths (
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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.