Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.

ABSTRACT: Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with reproducible prediction performance on independent datasets. We investigated the best approaches to integrate different studies of the same type of omics data under a variety of different heterogeneities. We developed a comprehensive workflow to simulate a variety of different types of heterogeneity and evaluate the performances of different integration methods together with batch normalization by using ComBat. We also demonstrated the results through realistic applications on six

SUBMITTER: Gao Y 

PROVIDER: S-EPMC10602384 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature