Sparse superlayered neural network-based multi-omics cancer subtype classification

Abstract

Recently, targeted treatment of different subtypes of cancer has become of interest. To that end, we present a new deep neural network model, Sparse CRossmodal Superlayered Neural Network (SCR-SNN), for integrating high-dimensional RNA sequencing data with DNA methylation data. Our model consists of the following steps: (1) biomarker filtration; (2) biomarker selection, using a cross-modal, superlayered neural network with an L1 penalty; (3) integration of selected biomarkers from gene expression and DNA methylation data; and (4) prediction model building. For comparison, machine learning methods were used, alone and in combination. In these analyses, SCR-SNN was applied to gene expression and methylation data of lung adenocarcinoma and squamous cell lung carcinoma from The Cancer Genomic Atlas. The SCR-SNN model well classified lung cancer subtypes, using only a small number of markers. This approach represents a promising methodology for disease categorisation and diagnosis.