PRER: A patient representation with pairwise relative expression of proteins on biological networks.

ABSTRACT: Changes in protein and gene expression levels are often used as features in predictive modeling such as survival prediction. A common strategy to aggregate information contained in individual proteins is to integrate the expression levels with the biological networks. In this work, we propose a novel patient representation where we integrate proteins' expression levels with the protein-protein interaction (PPI) networks: Patient representation with PRER (Pairwise Relative Expressions with Random walks). PRER captures the dysregulation patterns of proteins based on the neighborhood of a protein in the PPI network. Specifically, PRER computes a feature vector for a patient by comparing the source protein's expression level with other proteins' levels that are within its neighborhood. The nei

SUBMITTER: Kuru HI 

PROVIDER: S-EPMC8238204 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature