DGLinker: flexible knowledge-graph prediction of disease-gene associations.

ABSTRACT: As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotyp

SUBMITTER: Hu J 

PROVIDER: S-EPMC8262728 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature