Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI.

ABSTRACT:

Background

DNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific workflow for joint data processing.

Results

We propose a comprehensive approach of combined DNA methylation datasets to classify controls and patients. The solution includes data harmonization, construction of machine learning classification models, dimensionality reduction of models, imputation of missing values, and explanation of model predictions by explainable artificial intelligence (XAI) algorithms. We show that harmonization can improve classification accuracy by up to 20% when preprocessing methods of the training and test datasets are different. The best accuracy re

SUBMITTER: Kalyakulina A 

PROVIDER: S-EPMC9718659 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature