ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization.

ABSTRACT:

Background

In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species.

Results

We introduce ECDEP, an essential protein identification model based on evolutionary community discovery. ECDEP integrates temporal gene expression data with a protein-protein interaction (PPI) network and employs the 3-Sigma rule to eliminate outliers at each time point, constructing a dynamic network. Next, we utilize edge birth and death information to establish an int

SUBMITTER: Ye C 

PROVIDER: S-EPMC10821549 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature