Computational Intelligence and Algorithmic Advances in Genome Editing
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 106
Special Issue Editor
Special Issue Information
Dear Colleagues,
Genome editing technologies, particularly CRISPR-based systems, have rapidly transitioned from experimental tools to central platforms in biotechnology, medicine, and synthetic biology. Despite their transformative potential, critical challenges remain regarding editing efficiency, target specificity, scalability, and safety. Addressing these challenges increasingly depends on the integration of computational methods capable of guiding experimental design, predicting molecular behavior, and interpreting complex genomic data.
This Special Issue, entitled “Computational Intelligence and Algorithmic Advances in Genome Editing,” aims to highlight state-of-the-art computational strategies that support, enhance, and redefine genome engineering workflows. We seek to publish contributions that bridge algorithm development, molecular modeling, and data-driven approaches with practical genome editing applications.
Original research articles and comprehensive reviews are welcome, including—but not limited to—studies focused on algorithmic innovations for CRISPR systems, machine learning models for outcome prediction, computational assessment of off-target effects, structural and dynamical modeling of editing complexes, and integrative bioinformatics pipelines for post-editing genomic analysis. By bringing together computational scientists and experimental biologists, this Special Issue aims to foster cross-disciplinary solutions that will shape the next generation of genome editing technologies.
Dr. Anderson Rodrigues dos Santos
Guest Editor
Manuscript Submission Information
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Keywords
- CRISPR/cas systems
- genome engineering
- bioinformatics
- gRNA design
- off-target prediction
- machine learning
- structural modeling
- molecular informatics
- DNA repair pathways
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