Tensor Decomposition for Subtyping of Complex Diseases based on Clinical and Genomic Data It has long been understood that stratification of patients into finegrained cohorts is a foundation of accurate diagnosis and effective treatment of complex diseases, such as cancer. Nevertheless, cancer therapies still fail or cause unnecessary suffering to many patients, which suggests that our current understanding of cancer subtypes needs to be refined. In this paper, we propose CLIGEN, a novel computational pipeline for highthroughput datadriven stratification of patients with a complex disease into cohorts corresponding to multimodal disease subtypes based on clinical and genomic data. We applied CLIGEN to discover breast cancer subtypes based on the clinical and genomic data of 503 patients with breast ductal carcinoma in the Cancer Genome Atlas (TCGA). Quantitative and qualitative evaluation of the breast cancer subtypes discovered by CLIGEN indicate that they are biologically meaningful and correlate with clinical outcomes, such as patient survival time.

OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data The discovery of important biomarkers is a significant step towards understanding the molecular mechanisms of carcinogenesis; enabling accurate diagnosis for, and prognosis of, a certain cancer type. Before recommending any diagnosis, genomics data such as gene expressions (GE) and clinical outcomes need to be analyzed. However, complex nature, high dimensionality, and heterogeneity in genomics data make the overall analysis challenging. Convolutional neural networks (CNN) have shown tremendous success in solving such problems. However, neural network models are perceived mostly as u0027black boxu0027 methods because of their not wellunderstood internal functioning. However, interpretability is important to provide insights on why a given cancer case has a certain type. Besides, finding the most important biomarkers can help in recommending more accurate treatments and drug repositioning. Moreover, the u0027right to explanationu0027 of the EU GDPR gives patients the right to know why and how an algorithm made a diagnosis decision. Hence, in this paper, we propose a new approach called OncoNetExplainer to make explainable predictions of cancer types based on GE data. We used genomics data about 9,074 cancer patients covering 33 different cancer types from the PanCancer Atlas on which we trained CNN and VGG16 networks using guidedgradient class activation maps (GradCAM). Further, we generate classspecific heat maps to identify significant biomarkers and computed feature importance in terms of mean absolute impact to rank top genes across all the cancer types. Quantitative and qualitative analyses show that both models exhibit high confidence at predicting the cancer types correctly giving an average precision of 96.25%. To provide comparisons with the baselines, we identified top genes, and cancerspecific driver genes using gradient boosted trees and SHapley Additive exPlanations (SHAP). Finally, our findings were validated with the annotations provided by the TumorPortal.