Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.

ABSTRACT:

Background

Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes.

Results

Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the perso

SUBMITTER: Huang Y 

PROVIDER: S-EPMC10804660 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature