AttOmics: attention-based architecture for diagnosis and prognosis from omics data.

ABSTRACT:

Motivation

The increasing availability of high-throughput omics data allows for considering a new medicine centered on individual patients. Precision medicine relies on exploiting these high-throughput data with machine-learning models, especially the ones based on deep-learning approaches, to improve diagnosis. Due to the high-dimensional small-sample nature of omics data, current deep-learning models end up with many parameters and have to be fitted with a limited training set. Furthermore, interactions between molecular entities inside an omics profile are not patient specific but are the same for all patients.

Results

In this article, we propose AttOmics, a new deep-learning architecture based on the self-attention mechanism. First, we decompose each omics profile into a

SUBMITTER: Beaude A 

PROVIDER: S-EPMC10311315 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature