Importance of Feature Weighing in Cervical Cancer Subtypes Identification Cancer subtypes identification is very important for the advancement of precision cancer disease diagnosis and therapy. It is one of the important components of the personalized medicine framework. Cervical cancer (CC) is one of the leading gynecological cancers that causes deaths in women worldwide. However, there is a lack of studies to identify histological subtypes among the patients suffering from tumor of the uterine cervix. Hence, subtyping of cancer can help in analyzing shared molecular profiles between different histological subtypes of solid tumors of uterine cervix. With the advancement in technology, large scale multiomics data are generated. The integration of genomics data generated from different platforms helps in capturing complementary information about the patients. Several computational approaches have been developed that integrate mutiomics data for cancer subtyping. In this study, mRNA (messenger RNA) and miRNA (microRNA) expression data are integrated to identify the histological subtypes of CC. In this regard, a method is proposed that ranks the biomarkers (mRNA and miRNA) on the basis of their varying expression across the samples. The ranking method generates a weight for every biomarker which is further used to identify the similarity between the samples. A wellknown approach named Similarity Network Fusion (SNF) is then applied, followed by Spectral clustering, to identify groups of related samples. This study focuses on the role of weighing the biomarkers prior to their integration and application of the clustering algorithm. The weighing method proposed in this study is compared with some other methods and proved to be more efficient. The proposed method helps in identifying histological subtypes of CC and can also be applied to other types of cancer data where histological subtypes play a key role in designing treatments and therapies.

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.