Deep learning for cancer type classification and driver gene identification.

ABSTRACT:

Background

Genetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. We aim to develop a novel method to effectively explore the landscape of genetic variants, including germline variants, and small insertions and deletions for cancer type prediction.

Results

We proposed DeepCues, a deep learning model that utilizes convolutional neural networks to unbiasedly derive features from raw cancer DNA sequencing data for disease classification and relevant gene discovery. Using raw whole-exome sequencing as features, germline variants and somatic mutations, inc

SUBMITTER: Zeng Z 

PROVIDER: S-EPMC8543824 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature