Authentication of differential gene expression in oral squamous cell carcinoma using machine learning applications.

ABSTRACT:

Background

Recently, the possibility of tumour classification based on genetic data has been investigated. However, genetic datasets are difficult to handle because of their massive size and complexity of manipulation. In the present study, we examined the diagnostic performance of machine learning applications using imaging-based classifications of oral squamous cell carcinoma (OSCC) gene sets.

Methods

RNA sequencing data from SCC tissues from various sites, including oral, non-oral head and neck, oesophageal, and cervical regions, were downloaded from The Cancer Genome Atlas (TCGA). The feature genes were extracted through a convolutional neural network (CNN) and machine learning, and the performance of each analysis was compared.

Results

The ability of the machine

SUBMITTER: Pratama R 

PROVIDER: S-EPMC8164276 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature