Classifying the multi-omics data of gastric cancer using a deep feature selection method

Gastric cancer has the highest incidence among all types of malignant tumors. The rapid development of high throughput gene technology has greatly promoted people's understanding of gastric cancer at the molecular level. However, there is a lack of information in single omics data, so dimensionality reduction is an effective method to overcome the dimensionality disaster of omics data. omics data has the characteristics of being multivariate and high-dimensional, which affects the efficiency of classification. Therefore, dimensionality reduction is an effective method to overcome the dimensionality disaster of omics data. However, neural network learning algorithm is seldom used to improve classification accuracy when feature selection of multiomics data is carried out, therefore, in this study, a random forest deep feature selection (RDFS) algorithm was proposed. By integrating gene expression (Exp) data and copy number variation (CNV) data, the dimensions of multi-omics data were reduced and improve the classification accuracy by using a random forest and deep neural network. The results showed that the accuracy and area under the curve (AUC) of multi-omics data were better than that of single-omics data under the RDFS algorithm. With other feature selection algorithms, RDFS also had a higher prediction accuracy and AUC. We also validated the effect of feature selection on RDFS. Finally, survival analysis was used to evaluate the important genes identified during feature selection and to obtain enrichment gene ontology (GO) terms and biological pathways for these genes.