MTM: a multi-task learning framework to predict individualized tissue gene expression profiles.

ABSTRACT:

Motivation

Transcriptional profiles of diverse tissues provide significant insights in both fundamental and translational researches, while transcriptome information is not always available for tissues that require invasive biopsies. Alternatively, predicting tissue expression profiles from more accessible "surrogate" samples, especially blood transcriptome, has become a promising strategy when invasive procedures are not practical. However, existing approaches ignore tissue-shared intrinsic relevance, inevitably limiting predictive performance.

Results

We propose a unified deep learning-based multi-task learning framework, multi-tissue transcriptome mapping (MTM), enabling the prediction of individualized expression profiles from any available tissue of an individual. By jo

SUBMITTER: He G 

PROVIDER: S-EPMC10278940 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature