MK-BMC: a Multi-Kernel framework with Boosted distance metrics for Microbiome data for Classification.

ABSTRACT:

Motivation

Research on human microbiome has suggested associations with human health, opening opportunities to predict health outcomes using microbiome. Studies have also suggested that diverse forms of taxa such as rare taxa that are evolutionally related and abundant taxa that are evolutionally unrelated could be associated with or predictive of a health outcome. Although prediction models were developed for microbiome data, no prediction models currently exist that use multiple forms of microbiome-outcome associations.

Results

We developed MK-BMC, a Multi-Kernel framework with Boosted distance Metrics for Classification using microbiome data. We propose to first boost widely used distance metrics for microbiome data using taxon-level association signal strengths to up-wei

SUBMITTER: Xu H 

PROVIDER: S-EPMC10789312 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature