Optimal Transport based Cross-Domain Integration for Heterogeneous Data.

ABSTRACT: Detecting dynamic patterns shared across heterogeneous datasets is a critical yet challenging task in many scientific domains, particularly within the biomedical sciences. Systematic heterogeneity inherent in diverse data sources can significantly hinder the effectiveness of existing machine learning methods in uncovering shared underlying dynamics. Additionally, practical and technical constraints in real-world experimental designs often limit data collection to only a small number of subjects, even when rich, time-dependent measurements are available for each individual. These limited sample sizes further diminish the power to detect common dynamic patterns across subjects. In this article, we propose a novel heterogeneous data integration framework based on optimal transport to extract

SUBMITTER: Yuan Y 

PROVIDER: S-EPMC12520601 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature