Inherently interpretable position-aware convolutional motif kernel networks for biological sequencing data.

ABSTRACT: Artificial neural networks show promising performance in detecting correlations within data that are associated with specific outcomes. However, the black-box nature of such models can hinder the knowledge advancement in research fields by obscuring the decision process and preventing scientist to fully conceptualize predicted outcomes. Furthermore, domain experts like healthcare providers need explainable predictions to assess whether a predicted outcome can be trusted in high stakes scenarios and to help them integrating a model into their own routine. Therefore, interpretable models play a crucial role for the incorporation of machine learning into high stakes scenarios like healthcare. In this paper we introduce Convolutional Motif Kernel Networks, a neural network architecture that in

SUBMITTER: Ditz JC 

PROVIDER: S-EPMC10567796 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature