Deciphering RNA splicing logic with interpretable machine learning.

ABSTRACT: Machine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uninterpretability: Despite their excellent accuracy, they cannot describe how they arrived at their predictions. Here, using an "interpretable-by-design" approach, we present a neural network model that provides insights into RNA splicing, a fundamental process in the transfer of genomic information into functional biochemical products. Although we designed our model to emphasize interpretability, its predictive accuracy is on par with state-of-the-art models. To demonstrate the model's interpretability, we introduce a visualization that, for any given

SUBMITTER: Liao SE 

PROVIDER: S-EPMC10576025 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature