AI-Driven Bioinformatics: Transforming Genomics, Gene Editing, and Personalized Medicine

Abstract: This research paper explores the transformative integration of artificial intelligence (AI) with bioinformatics to advance genomics, gene editing, and personalized medicine. It examines how machine learning, deep learning, and natural language processing techniques are revolutionizing the analysis of large-scale genomic data, enabling precise identification of disease-associated variants, and accelerating drug discovery. The study highlights AI-enhanced approaches to CRISPR-Cas9 guide RNA design, variant effect prediction, and patient-specific treatment planning, demonstrating how computational models are improving accuracy, scalability, and clinical translation. Through case studies and empirical findings, the paper illustrates the impact of AI in enabling precision diagnostics, predicting therapeutic outcomes, and supporting the development of individualized therapeutic regimens. By bridging computational biology and clinical practice, this work offers a comprehensive framework for leveraging AI to unlock the full potential of precision medicine in the era of genomic healthcare. Keywords: Artificial intelligence, Bioinformatics, Genomics, Gene editing, Personalized medicine, Machine learning, Deep learning, CRISPR, Variant prediction, Precision diagnostics, Therapeutic modeling, Genomic data analysis, Computational biology, AI in healthcare, Personalized therapeutics