AttCRISPR: a spacetime interpretable model for prediction of sgRNA on-target activity.

ABSTRACT: BACKGROUND:More and more Cas9 variants with higher specificity are developed to avoid the off-target effect, which brings a significant volume of experimental data. Conventional machine learning performs poorly on these datasets, while the methods based on deep learning often lack interpretability, which makes researchers have to trade-off accuracy and interpretability. It is necessary to develop a method that can not only match deep learning-based methods in performance but also with good interpretability that can be comparable to conventional machine learning methods. RESULTS:To overcome these problems, we propose an intrinsically interpretable method called AttCRISPR based on deep learning to predict the on-target activity. The advantage of AttCRISPR lies in using the ensemble learning

SUBMITTER: Xiao LM 

PROVIDER: S-EPMC8667445 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature