KG2ML: integrating knowledge graphs and positive unlabeled learning for identifying disease-associated genes.

ABSTRACT:

Background

Biomedical knowledge graphs (KGs), such as the Data Distillery Knowledge Graph (DDKG), capture known relationships among entities (e.g., genes, diseases, proteins), providing valuable insights for research. However, these relationships are typically derived from prior studies, leaving potential unknown associations unexplored. Identifying such unknown associations, including previously unknown disease-associated genes, remains a critical challenge in bioinformatics and is crucial for advancing biomedical knowledge.

Methods

Traditional methods, such as linkage analysis and genome-wide association studies (GWAS), can be time-consuming and resource-intensive. This highlights the need for efficient computational approaches to identify or predict new genes using known

SUBMITTER: Kumar P 

PROVIDER: S-EPMC12823822 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature