Identification of mortality-risk-related missense variant for renal clear cell carcinoma using deep learning.

ABSTRACT:

Introduction

Kidney renal clear cell carcinoma (KIRCC) is a highly heterogeneous and lethal cancer that can arise in patients with renal disease. DeepSurv combines a deep feed-forward neural network with a Cox proportional hazards function and could provide optimized survival results compared with convenient survival analysis.

Methods

This study used an improved DeepSurv algorithm to identify the candidate genes to be targeted for treatment on the basis of the overall mortality status of KIRCC subjects. All the somatic mutation missense variants of KIRCC subjects were abstracted from TCGA-KIRC database.

Results

The improved DeepSurv model (95.1%) achieved greater balanced accuracy compared with the DeepSurv model (75%), and identified 610 high-risk variants associated

SUBMITTER: Chen JB 

PROVIDER: S-EPMC7890720 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature