Cancer Subtype Classification based on Superlayered Neural Network Targeted treatment on different cancer subtype has been of clinical interest. However, accurate molecular identification of pathological cancer subtypes has been challenging. To meet such needs, a new subtype classification model based on deep neural network, Sparse Crossmodal Superlayered Neural Network is presented in this study. The model focuses on selecting biomarkers with considering integration of the high dimensional RNA sequencing data and DNA methylation data. For a real data application, the multiomics data of lung adenocarcinoma and squamous cell carcinoma from The Cancer Genomic Atlas was fitted to the proposed model. Our model was compared to other existing methods such as principal component analysis, penalized logistic regression, and artificial neural network. With only a small number of biomarkers, the proposed model was able to effectively classify these lung cancer subtypes. Gene set analysis of selected biomarkers revealed significant difference in epidermis development and cornification pathway activation level between two lung cancer subtypes.

Comparison of Different Classification Methods for Breast Cancer Subtypes Prediction Breast cancer is one of the most common cancers among women. Due to heterogeneity of cancers, breast cancer is divided into different subtypes. Different subtypes have different molecular genesis, so the corresponding target cells and treatment plans are different. Identifying the correct cancer subtypes is important for cancer diagnosis and prognosis. Breast cancer subtypes can be divided into four types: Basal, Her2, Luminal A, and Luminal B. Many machine learning approaches have been applied to cancer subtypes classification in the past few years, we present a comparison of different classifiers KNearest Neighbor (KNN), Support Vector Machine (SVM), MultiLayer Perception (MLP), and MultiGrained Cascade Forest (gcForest) on The Cancer Genome Atlas (TCGA) databases of breast cancer. As we all know, biological data are highdimensional and have small sample size, so before classification, we use subtype dependent feature selection method to reduce dimensionality of RNASeq gene expression data. Experimental results show that gcForest has a higher accuracy rate for breast cancer subtypes prediction compared with other classifiers.