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.

Multipath convolutional neural network for glioblastoma survival group prediction with point mutations and demographic features Glioblastoma multiforme (GBM) is the most common and aggressive brain cancer with a median survival rate of 15 months. It is wellestablished that age is a strong independent predictor of GBM survival outcome. There is accumulating evidence that single nucleotide polymorphisms (SNPs) in the IDH1 gene influences GBM survival time. We propose a new multipath convolutional neural network that combines SNPs, age, age groups, and gender to predict survival groups with a oneyear threshold. We obtained GBM SNP and demographic data from The Cancer Genome Atlas. We compare our multipath CNN with a support vector machine (SVM) and random forest. We randomly held out 10% of the samples as a test set, and employed 10fold crossvalidation for hyperparameter tuning in the remaining 90%. We then fit a model with optimal hyperparameters and predict the test set. In the combined SNP and demographic features, our proposed multipath model achieved 67% accuracy in the test set compared to SVM accuracy of 60% and random forest accuracy of 47%. In the 10fold crossvalidation, our model predicted the two survival groups with 63% mean balanced accuracy while SVM and random forest attained 56% and 49% mean balanced accuracy. We evaluated the predictive ability in combined SNP and demographic data versus each data source alone for our proposed CNN, SVM, and random forest. The highest achieved accuracy for SNP data only in the test data set is 60% with our singlepath CNN. The top accuracy in the test data set for demographic features alone attained is 60% by SVM and our singlepath neural network.