Article

Correcting for Background Noise Improves Phenotype Prediction from Human Gut Microbiome Data

2021-03-20

Abstract excerpt

The ability to predict human phenotypes accurately from metagenomic data is crucial for developing biomarkers and therapeutics for diseases. However, metagenomic data is commonly affected by technical or biological variables, unrelated to the phenotype of interest, such as sequencing protocol or host sex, which can greatly reduce or, when correlated to the phenotype of interest, inflate prediction accuracy. We per...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: ff48b915-7959-56db-a1d0-93cf92fe2f1e
DOI: 10.1101/2021.03.19.436199

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Correcting for Background Noise Improves Phenotype Prediction from Human Gut Microbiome DataDOI 10.1101/2021.03.19.436199Read

Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneity.Semantic proximity Read

Comparison of the effectiveness of different normalization methods for metagenomic cross-study phenotype prediction under heterogeneitySemantic proximity Read

MUSiCC: A marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiomeSemantic proximity Read

Effects of Data Transformation and Model Selection on Feature Importance in Microbiome Classification DataSemantic proximity Read

Statistical normalization methods in microbiome data with application to microbiome cancer researchSemantic proximity Read

metamicrobiomeR: an R package for analysis of microbiome relative abundance data using zero-inflated beta GAMLSS and meta-analysis across studies using random effect modelsSemantic proximity Read

MicrobiomeGWAS: a tool for identifying host genetic variants associated with microbiome compositionSemantic proximity Read

Self-supervised learning enables robust microbiome predictions in data-limited and cross-cohort settingsSemantic proximity Read

MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition.Semantic proximity Read

An Exponential Scale Mixture Model for Metatranscriptomics Data with Application to Inflammatory Bowel DiseaseSemantic proximity Read

Multivariable Association Discovery in Population-scale Meta-omics StudiesSemantic proximity Read

A realistic benchmark for the identification of differentially abundant taxa in (confounded) human microbiome studiesSemantic proximity Read