PhyloMix: enhancing microbiome-trait association prediction through phylogeny-mixing augmentation.

ABSTRACT:

Motivation

Understanding the associations between traits and microbial composition is a fundamental objective in microbiome research. Recently, researchers have turned to machine learning (ML) models to achieve this goal with promising results. However, the effectiveness of advanced ML models is often limited by the unique characteristics of microbiome data, which are typically high-dimensional, compositional, and imbalanced. These characteristics can hinder the models' ability to fully explore the relationships among taxa in predictive analyses. To address this challenge, data augmentation has become crucial. It involves generating synthetic samples with artificial labels based on existing data and incorporating these samples into the training set to improve ML model performance.<

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC11849959 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature