COmic: convolutional kernel networks for interpretable end-to-end learning on (multi-)omics data.

ABSTRACT:

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 in

SUBMITTER: Ditz JC 

PROVIDER: S-EPMC10311322 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature