MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model.

ABSTRACT:

Background

Accurately classifying complex diseases is crucial for diagnosis and personalized treatment. Integrating multi-omics data has been demonstrated to enhance the accuracy of analyzing and classifying complex diseases. This can be attributed to the highly correlated nature of the data with various diseases, as well as the comprehensive and complementary information it provides. However, integrating multi-omics data for complex diseases is challenged by data characteristics such as high imbalance, scale variation, heterogeneity, and noise interference. These challenges further emphasize the importance of developing effective methods for multi-omics data integration.

Results

We proposed a novel multi-omics data learning model called MODILM, which integrates multiple omi

SUBMITTER: Zhong Y 

PROVIDER: S-EPMC10161645 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature