Integrated Multiomics Analysis Using Variational Autoencoders: Application to Pancancer Classification Omics data are normally high dimensional with large number of molecular features and relatively small number of available samples with clinical labels. The xe2x80x9ccurse of dimensionalityxe2x80x9d makes it challenging to train a machine learning model using high dimensional omics data like DNA methylation and gene expression profiles. Here we propose an endtoend deep learning model called OmiVAE to extract low dimensional features and classify samples from multiomics data. OmiVAE combines the basic structure of variational autoencoders with a classifier to achieve taskoriented feature extraction and multiclass classification. The training procedure of OmiVAE is comprised of an unsupervised phase and a supervised phase. During the unsupervised phase, a hierarchical cluster structure of samples can be automatically formed without the need for labels. And in the supervised phase, OmiVAE achieved an average accuracy of 97.49% after 10fold crossvalidation among 33 tumour types and normal samples, which shows better performance than existing methods. The integrated model learned from multiomics datasets outperformed those using only one type of omics data, which indicates that the complementary information from different omics datatypes provides useful insights for biomedical tasks like cancer classification.

A Translational Pipeline for Overall Survival Prediction of Breast Cancer Patients by DecisionLevel Integration of MultiOmics Data Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival rates, indicating a need to identify prognostic biomarkers. By integrating multiomics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)), it is likely to improve the accuracy of patient survival predictions compared to prediction using single modality data. Therefore, we propose to develop a machine learning pipeline using decisionlevel integration of multiomics tumor data from The Cancer Genome Atlas (TCGA) to predict the overall survival of breast cancer patients. With multiomics data consisting of gene expression, methylation, miRNA expression, and CNVs, the topperforming model predicted survival with an accuracy of 85% and area under the curve (AUC) of 87%. Furthermore, the model was able to identify which modalities best contributed to prediction performance, identifying methylation, miRNA, and gene expression as the best integrated classification combination. Our method not only recapitulated several breast cancerspecific prognostic biomarkers that were previously reported in the literature but also yielded several novel biomarkers. Further analysis of these biomarkers could lend insight into the molecular mechanisms that lead to poor survival.