Deep fusion of incomplete multi-omic data for molecular mechanism of Alzheimer's disease.

ABSTRACT: Multi-omics data provides a comprehensive view of biological systems and enables researchers to uncover intricate molecular mechanisms underlying complex diseases. However, multi-omic data is often incomplete and joint modeling of multi-omics data will lead to exclusion of a large portion of subjects. Furthermore, most current multi-omics studies pinpoint individual -omics markers, which may not interact, posing challenges for interpretation. In this study, we developed an interpretable deep trans-omic fusion neural network, TransFuse, to include incomplete -omic data for training of prediction models. When evaluated using the data from two Alzheimer's disease cohorts, TransFuse generally showed superior or comparable performance over competing methods in a wide range of metrics like class

SUBMITTER: Xie L 

PROVIDER: S-EPMC12361453 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature