Identifying cellular cancer mechanisms through pathway-driven data integration.

ABSTRACT:

Motivation

Cancer is a genetic disease in which accumulated mutations of driver genes induce a functional reorganization of the cell by reprogramming cellular pathways. Current approaches identify cancer pathways as those most internally perturbed by gene expression changes. However, driver genes characteristically perform hub roles between pathways. Therefore, we hypothesize that cancer pathways should be identified by changes in their pathway-pathway relationships.

Results

To learn an embedding space that captures the relationships between pathways in a healthy cell, we propose pathway-driven non-negative matrix tri-factorization. In this space, we determine condition-specific (i.e. diseased and healthy) embeddings of pathways and genes. Based on these embeddings, we defin

SUBMITTER: Windels SFL 

PROVIDER: S-EPMC9477533 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature