An Ovarian Cancer Susceptible Gene Prediction Method Based on Deep Learning Methods.

ABSTRACT: Ovarian cancer (OC) is one of the most fatal diseases among women all around the world. It is highly lethal because it is usually diagnosed at an advanced stage which may reduce the survival rate greatly. Even though most of the patients are treated timely and effectively, the survival rate is still low due to the high recurrence rate of OC. With a large number of genome-wide association analysis (GWAS)-discovered risk regions of OC, expression quantitative trait locus (eQTL) analyses can explore candidate susceptible genes based on these risk loci. However, a large number of OC-related genes remain unknown. In this study, we proposed a novel gene prediction method based on different omics data and deep learning methods to identify OC causal genes. We first employed graph attention network

SUBMITTER: Ye L 

PROVIDER: S-EPMC8414800 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature