Autoencoders with shared and specific embeddings for multi-omics data integration.

ABSTRACT:

Background

In cancer research, different levels of high-dimensional data are often collected for the same subjects. Effective integration of these data by considering the shared and specific information from each data source can help us better understand different types of cancer.

Results

In this study we propose a novel autoencoder (AE) structure with explicitly defined orthogonal loss between the shared and specific embeddings to integrate different data sources. We compare our model with previously proposed AE structures based on simulated data and real cancer data from The Cancer Genome Atlas. Using simulations with different proportions of differentially expressed genes, we compare the performance of AE methods for subsequent classification tasks. We also compare the mo

SUBMITTER: Wang C 

PROVIDER: S-EPMC12362917 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature