A graph neural network approach for hierarchical mapping of breast cancer protein communities.

ABSTRACT:

Background

Comprehensively mapping the hierarchical structure of breast cancer protein communities and identifying potential biomarkers from them is a promising way for breast cancer research. Existing approaches are subjective and fail to take information from protein sequences into consideration. Deep learning can automatically learn features from protein sequences and protein-protein interactions for hierarchical clustering.

Results

Using a large amount of publicly available proteomics data, we created a hierarchical tree for breast cancer protein communities using a novel hierarchical graph neural network, with the supervision of gene ontology terms and assistance of a pre-trained deep contextual language model. Then, a group-lasso algorithm was applied to identify prote

SUBMITTER: Zhang X 

PROVIDER: S-EPMC11749236 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature