MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification.

ABSTRACT:

Motivation

Accurate diagnostic classification and biological interpretation are important in biology and medicine, which are data-rich sciences. Thus, integration of different data types is necessary for the high predictive accuracy of clinical phenotypes, and more comprehensive analyses for predicting the prognosis of complex diseases are required.

Results

Here, we propose a novel multi-task attention learning algorithm for multi-omics data, termed MOMA, which captures important biological processes for high diagnostic performance and interpretability. MOMA vectorizes features and modules using a geometric approach and focuses on important modules in multi-omics data via an attention mechanism. Experiments using public data on Alzheimer's disease and cancer with various cla

SUBMITTER: Moon S 

PROVIDER: S-EPMC10060719 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature