Balanced Training Sets Improve Deep Learning-Based Prediction of CRISPR sgRNA Activity.

ABSTRACT: CRISPR-Cas systems have transformed the field of synthetic biology by providing a versatile method for genome editing. The efficiency of CRISPR systems is largely dependent on the sequence of the constituent sgRNA, necessitating the development of computational methods for designing active sgRNAs. While deep learning-based models have shown promise in predicting sgRNA activity, the accuracy of prediction is primarily governed by the data set used in model training. Here, we trained a convolutional neural network (CNN) model and a large language model (LLM) on balanced and imbalanced data sets generated from CRISPR-Cas12a screening data for the yeast Yarrowia lipolytica and evaluated their ability to predict high- and low-activity sgRNAs. We further tested whether prediction performa

SUBMITTER: Trivedi V 

PROVIDER: S-EPMC11574921 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature