  Motivation: The size of available omics datasets is steadily increasing with
technological advancement in recent years. While this increase in sample size
can be used to improve the performance of relevant prediction tasks in
healthcare, models that are optimized for large datasets usually operate as
black boxes. In high stakes scenarios, like healthcare, using a black-box model
poses safety and security issues. Without an explanation about molecular
factors and phenotypes that affected the prediction, healthcare providers are
left with no choice but to blindly trust the models. We propose a new type of
artificial neural network, named Convolutional Omics Kernel Network (COmic). By
combining convolutional kernel networks with pathway-induced kernels, our
method enables robust and interpretable end-to-end learning on omics datasets
ranging 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 different
breast cancer cohorts. Additionally, we trained COmic models on multi-omics
data using the METABRIC cohort. Our models performed either better or similar
to competitors on both tasks. We show how the use of pathway-induced Laplacian
kernels opens the black-box nature of neural networks and results in
intrinsically interpretable models that eliminate the need for post-hoc
explanation models.
