GRACKLE: an interpretable matrix factorization approach for biomedical representation learning.

ABSTRACT:

Motivation

Disruption in normal gene expression can contribute to the development of diseases and chronic conditions. However, identifying disease-specific gene signatures can be challenging due to the presence of multiple co-occurring conditions and limited sample sizes. Unsupervised representation learning methods, such as matrix decomposition and deep learning, simplify high-dimensional data into understandable patterns, but often do not provide clear biological explanations. Incorporating prior biological knowledge directly can enhance understanding and address small sample sizes. Nevertheless, current models do not jointly consider prior knowledge of molecular interactions and sample labels.

Results

We present GRACKLE, a novel nonnegative matrix factorization approach t

SUBMITTER: Gillenwater LA 

PROVIDER: S-EPMC12261436 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature