Classification of Cancer Types Using Graph Convolutional Neural Networks.

ABSTRACT:

Background

Cancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently.

Results

In this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal. Four GCNN models based o

SUBMITTER: Ramirez R 

PROVIDER: S-EPMC7799442 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature