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Motivation: The size of available omics datasets is steadily increasing withtechnological advancement in recent years. While this increase in sample sizecan be used to improve the performance of relevant prediction tasks inhealthcare, models that are optimized for large datasets usually operate asblack boxes. In high stakes scenarios, like healthcare, using a black-box modelposes safety and security issues. Without an explanation about molecularfactors and phenotypes that affected the prediction, healthcare providers areleft with no choice but to blindly trust the models. We propose a new type ofartificial neural network, named Convolutional Omics Kernel Network (COmic). Bycombining convolutional kernel networks with pathway-induced kernels, ourmethod enables robust and interpretable end-to-end learning on omics datasetsranging in size from a few hundred to several hundreds of thousands of samples.Furthermore, COmic can be easily adapted to utilize multi-omics data.  Results: We evaluated the performance capabilities of COmic on six differentbreast cancer cohorts. Additionally, we trained COmic models on multi-omicsdata using the METABRIC cohort. Our models performed either better or similarto competitors on both tasks. We show how the use of pathway-induced Laplaciankernels opens the black-box nature of neural networks and results inintrinsically interpretable models that eliminate the need for post-hocexplanation models.

COmic: Convolutional Kernel Networks for Interpretable End-to-End Learning on (Multi-)Omics Data