  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 `black
box' methods because of their not well-understood 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. 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
Pan-Cancer Atlas on which we trained CNN and VGG16 networks using
guided-gradient class activation maps++(GradCAM++). Further, we generate
class-specific 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 cancer-specific driver genes using gradient boosted
trees and SHapley Additive exPlanations(SHAP). Finally, our findings were
validated with the annotations provided by the TumorPortal.
