Heterogeneous network embedding for identifying symptom candidate genes.

ABSTRACT:

Objective

Investigating the molecular mechanisms of symptoms is a vital task in precision medicine to refine disease taxonomy and improve the personalized management of chronic diseases. Although there are abundant experimental studies and computational efforts to obtain the candidate genes of diseases, the identification of symptom genes is rarely addressed. We curated a high-quality benchmark dataset of symptom-gene associations and proposed a heterogeneous network embedding for identifying symptom genes.

Methods

We proposed a heterogeneous network embedding representation algorithm, which constructed a heterogeneous symptom-related network that integrated symptom-related associations and applied an embedding representation algorithm to obtain the low-dimensional vector re

SUBMITTER: Yang K 

PROVIDER: S-EPMC7646926 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature