AI-Driven Bioinformatics Framework for CRISPR-Based Genetic Analysis

Abstract

Modern advances in gene editing technologies, such as CRISPR, have revolutionized researches in genes and their applications. However, CRISPR systems are so complicated that the need for a good bioinformatics system arises in order to process high-scale genomic data. In the paper, an AI-based bioinformatics system for CRISPR-based genomics is presented. First, the problem addressed by the bioinformatics system is to detect CRISPR targets and predict off-target effects for their accurate and safe gene editing. The system methodology includes the use of machine learning algorithms for predicting CRISPR targets and off-target binding, as well as deep learning for increasing gene-editing effectiveness. Statistical data obtained after analyzing datasets were utilized in order to enhance the performance of the model. Important metrics like sensitivity, specificity, and accuracy have been used to measure the effectiveness of the proposed model. The results show that the prediction accuracy is considerably higher than the existing models in the field by 15 per cent. Furthermore, the model proves to be better than the existing techniques in identifying potential genetic alterations while minimizing off-target effects. All in all, the proposed model could contribute to the effectiveness and precision of genetic analysis with CRISPR system, making it an effective tool for genetic specialists. The proposed model makes the process of gene editing easier and faster, allowing genetic researchers and practitioners to conduct their investigations much quicker and with less risk. The future research direction could be in expanding the scope of the model's application in the field of genomics and improving its efficiency in processing genetic data.