Randomized Feature Selection Based Semi-supervised Latent Dirichlet Allocation For Microbiome Analysis

This article discusses the use of a novel semi-supervised methodology, RFSLDA, to study the impact of the gut microbiome on an individual’s health status. The method combines topic modeling and feature selection to classify individuals based on their microbiome data and observed health status. The results show that this approach is effective and efficient in comparison to traditional supervised learning methods.