MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.

ABSTRACT: The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the microbiota as a promising indicator or predictor with machine learning (ML) methods. However, the accuracy is impacted by a lot of factors when predicting host phenotypes with the metagenomic data, e.g. small sample size, class imbalance, high-dimensional features, etc. To address these challenges, we propose MicroHDF, an interpretable deep learning framework to predict host phenotypes, where a cascade layers of deep forest units is designed for handling sample class imbalance and high dimensional features. The experimental results show that the performance of MicroHDF is competitive with that of existing state-of-the-art methods on 13 publi

SUBMITTER: Shi K 

PROVIDER: S-EPMC11500453 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature