Overview of data preprocessing for machine learning applications in human microbiome research.

ABSTRACT: Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on

SUBMITTER: Ibrahimi E 

PROVIDER: S-EPMC10588656 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature