TaxaHFE: a machine learning approach to collapse microbiome datasets using taxonomic structure.

ABSTRACT:

Motivation

Biologists increasingly turn to machine learning models not just to predict, but to explain. Feature reduction is a common approach to improve both the performance and interpretability of models. However, some biological datasets, such as microbiome data, are inherently organized in a taxonomy, but these hierarchical relationships are not leveraged during feature reduction. We sought to design a feature engineering algorithm to exploit relationships in hierarchically organized biological data.

Results

We designed an algorithm, called TaxaHFE, to collapse information-poor features into their higher taxonomic levels. We applied TaxaHFE to six previously published datasets and found, on average, a 90% reduction in the number of features (SD = 5.1%) compared to using

SUBMITTER: Oliver A 

PROVIDER: S-EPMC10689668 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature