Classification of stomach cancer gene expression data using CNN algorithm of deep learning

Abstract

The incidence of stomach cancer has been found to be gradually decreasing; however, it remains one of the most frequently occurring malignant cancers in Korea. According to statistics of 2017, stomach cancer is the top cancer in men and the fourth most important cancer in women, necessitating methods for its early detection and treatment. Considerable research in the field of bioinformatics has been conducted in cancer studies, and bioinformatics approaches might help develop methods and models for its early prediction. We aimed to develop a classification method based on deep learning and demonstrate its application to gene expression data obtained from patients with stomach cancer. Data of 60,483 genes from 334 patients with stomach cancer in The Cancer Genome Atlas were evaluated by principal component analysis, heatmaps, and the convolutional neural network (CNN) algorithm. We combined the RNA-seq gene expression data with clinical data, searched candidate genes, and analyzed them using the CNN deep learning algorithm. We performed learning using the sample type and vital status of patients with stomach cancer and verified the results. We obtained an accuracy of 95.96% for sample type and 50.51% for vital status. Despite overfitting owing to the limited number of patients, relatively accurate results for sample type were obtained. This approach can be used to predict the prognosis of stomach cancer, which has many types and underlying causes.

A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree

“…Stomach tumor gene expression data using CNN classification procedure was developed based on deep learning approach, 60,000 data made up of stomach tumor genes were evaluated using PCA), heatmaps, and CNN algorithms with an accuracy of 96% and 51% [14]. RNA-Seq hidden transcripts in malaria parasites was proposed by relating variations of procedures to deconvolute transcriptional differences for distinct mosquitos and revealed hidden distinct transcriptional signatures [15].…”

Predicting RNA-seq data using genetic algorithm and ensemble classification algorithms

“…Sc-Pred RNA-seq dataset from pancreatic muscle, mixing dendritic cells, colorectal tumour material elimination, and mononuclear cells were applied and presented a high-performance accuracy [12]. RNA-DNA machine learning investigation showing low genome expressions influencing PAH ailment was proposed, using an advanced feature selection and enhanced machine learning procedure for classifying irrelevant but very beneficial genes, the results displayed clusters of unrelated expression genes that reveal predicting and distinctive transformed PAH [13]. Classification of gene expression gastrointestinal tumor dataset using deep learning approach was proposed, using about 60,000 genes from 334 gastrointestinal tumor patient's data, PCA, heatmaps, and the CNN algorithm were proposed using scientific, and RNA-seq gene expression data investigation and classification accuracy of 95.96% and 50.51% were achieved [14].…”

An Efficient PCA Ensemble Learning Approach for Prediction of RNA-Seq Malaria Vector Gene Expression Data Classification

“…Shon, Yi, Kim, Cha and Kim [14] worked on classifying gene expression stomach cancer data using CNN. They developed a classification technique based on deep learning and proved its application to data expression gotten from stomach cancer patients.…”

“…Problems with linear dimensionality reduction procedures is absorbing unrelated data facts in a lower dimensional section. PCA can visualize models and advance the clarification capability [14].…”

“…The illustration of the experimental value in this principal subspace develops a feature vector of detected values. Adopting [14], the sample mean x̄ and data covariance matrix S are as follows.…”