Classifying Sarcoma Cancer Using Deep Neural Networks Based on Multi-Omics Data

Abstract

The challenge in classifying cancer may lead to inaccurate classification of cancers, especially sarcoma cancer since it consists of rare types of cancer. It is hard for the clinician to confirm the patient's condition because the specialist pathology can only make an accurate diagnosis.  Therefore, instead of a single omics being used to identify the disease marker, integrating these omics to represent multi-omics brings more advantages in detecting and presenting the phenotype of the cancers. Nowadays, the advancement of computational models, especially deep learning, offered promising approaches in solving high-level omics of data with faster processing speed. Hence, the purpose of this study is to classify cancer and non-cancerous patients using Stacked Denoising Autoencoder (SDAE) and One-dimensional Convolutional Neural Network (1D CNN) to evaluate which algorithm classifies better using high correlated multi-omics data. The study employed both computational models to fit the multi-omics dataset. Sarcoma omics datasets used in this study was obtained from the Multi-Omics Cancer Benchmark TCGA Pre-processed Data of ACGT Ron Shamir Lab repository. The results obtained for the SDAE was 50.93% and 52.78% for the 1D CNN. The results show 1D CNN model outperformed SDAE in classifying sarcoma cancer.