An intrinsically interpretable neural network architecture for sequence-to-function learning.

ABSTRACT:

Motivation

Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called totally interpretable sequence-to-function model (tiSFM). tiSFM improves upon the performance of standard multilayer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multilayer neural network, internal model parameters are intrinsically interpretable in terms of relevant

SUBMITTER: Balcı AT 

PROVIDER: S-EPMC10311317 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature