Enhanced sgRNA On-Target Cleavage Efficacy Prediction using Conditional GANs

The wide usage of the Clustered Regularly Inter-spaced Short Palindromic Repeats associated with the Cas9 enzyme (CRISPR/Cas9) system, which is one of the latest genome-editing methods, has shed light on pathways that were inconceivable before, from enhancing fruit nutrition to treating incurable diseases. The precise prediction of single guide RNA (sgRNA) on-target knockout efficacy poses a substantial challenge to the practical application of CRISPR/Cas9 systems. Although many Machine Learning (ML)-based techniques have yielded encouraging results, prediction accuracy still needs improvement. CRISPR/Cas9 datasets do not contain many examples for training the models. Generative Adversarial Networks (GAN)s are good at learning a model for generating new training data given a dataset. This work proposes a novel Machine Learning model that combines Convolutional Neural Networks and conditional GANs (coGAN)s to predict sgRNA on-target knockout efficacy. Experimental results showed that the proposed model could outperform other models proposed in the literature in regression and classification tasks for predicting the on-target sgRNA cleavage efficacy in CRISPR/Cas9 systems.