Predicting candidate genes from phenotypes, functions and anatomical site of expression.

ABSTRACT:

Motivation

Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease-gene prioritization task. These methods generally compute the similarity between a patient's phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms, such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine-learning models.

Results

We developed a novel graph-based machine-lear

SUBMITTER: Chen J 

PROVIDER: S-EPMC8248315 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature