Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis.

ABSTRACT: Proteins are polypeptides essential in biological processes. Protein physical interactions are complemented by other types of functional relationship data including genetic interactions, knowledge about co-expression, and evolutionary pathways. Existing algorithms integrate protein interaction and gene expression data to retrieve context-specific subnetworks composed of genes/proteins with known and unknown functions. However, most protein function prediction algorithms fail to exploit diverse intrinsic information in feature and label spaces. We develop a novel integrative method based on differential Co-expression analysis and Neighbor-voting algorithm for Protein Function Prediction, namely CNPFP. The method integrates heterogeneous data and exploits intrinsic and latent linkages via gl

SUBMITTER: Wekesa JS 

PROVIDER: S-EPMC8030663 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature