About this Research Topic
Genome editing technologies are becoming more accessible and commonly used in a vast number of research fields, providing an increasing number of available datasets. While there have been significant advancements on the technological and experimental fronts, the computational side is still in its developmental stages and rapidly evolving.
Bioinformatic software and computational biology research will accelerate discoveries and technological advancements, creating new possibilities for understanding and manipulating genomes. Artificial intelligence (AI) and Machine Learning (ML) algorithms, used to perform tasks such as pattern recognition, classification, and predictions, have been successfully used to improve the efficiency and precision of CRISPR-based experiments, predict the most effective sites for gene editing, minimize off-target effects and maximize success rates.
Computational progress in genome editing will not only accelerate scientific discoveries but also open up new possibilities for gene therapies, crop improvements, and biomedical research.
This Research Topic aims to present developments and innovations in bioinformatic software and computational methods to analyze CRISPR-based datasets. In addition, it aims to explore the application of artificial intelligence (AI) in genome editing including target selection, off-target validation, new cas discovery, and mutant analysis. Our objective is to elucidate how AI empowers researchers and institutions through applications in genome editing for human health and disease treatment. The leveraging of AI enhances genomic capabilities, optimizing precision and expediting the identification of beneficial genetic modifications. In summary, this research topic aspires to be a comprehensive resource for researchers to delve into and examine the practical implications and potential impact of employing artificial intelligence (AI)-based omics-data analysis techniques to facilitate and optimize CRISPR-based strategies.
• Research articles focusing on AI models for genome editing
• Research articles focusing on software and bioinformatic tools for genome editing data analysis
• Research articles focusing on the application of AI for genome editing
• Review articles on advanced genome editing AI methods and computational tools
• Review articles focusing on the application of AI for genome editing
Bioinformatic software and computational biology research will accelerate discoveries and technological advancements, creating new possibilities for understanding and manipulating genomes. Artificial intelligence (AI) and Machine Learning (ML) algorithms, used to perform tasks such as pattern recognition, classification, and predictions, have been successfully used to improve the efficiency and precision of CRISPR-based experiments, predict the most effective sites for gene editing, minimize off-target effects and maximize success rates.
Computational progress in genome editing will not only accelerate scientific discoveries but also open up new possibilities for gene therapies, crop improvements, and biomedical research.
This Research Topic aims to present developments and innovations in bioinformatic software and computational methods to analyze CRISPR-based datasets. In addition, it aims to explore the application of artificial intelligence (AI) in genome editing including target selection, off-target validation, new cas discovery, and mutant analysis. Our objective is to elucidate how AI empowers researchers and institutions through applications in genome editing for human health and disease treatment. The leveraging of AI enhances genomic capabilities, optimizing precision and expediting the identification of beneficial genetic modifications. In summary, this research topic aspires to be a comprehensive resource for researchers to delve into and examine the practical implications and potential impact of employing artificial intelligence (AI)-based omics-data analysis techniques to facilitate and optimize CRISPR-based strategies.
• Research articles focusing on AI models for genome editing
• Research articles focusing on software and bioinformatic tools for genome editing data analysis
• Research articles focusing on the application of AI for genome editing
• Review articles on advanced genome editing AI methods and computational tools
• Review articles focusing on the application of AI for genome editing
Keywords: computational methods, bioinformatics, AI for genome editing, CRISPR/Cas9, off-target analysis, target selection
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