Artificial intelligence-driven epigenetic CRISPR therapeutics: a structured multi-domain meta-analysis of therapeutic efficacy, off-target prediction, and gRNA optimization

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dc.contributor.authorBasarali, Mustafa Kemal
dc.contributor.authorDaemi, Amin
dc.contributor.authorTahiraga, Ruhiyya Guliyeva
dc.contributor.authorOzbolat, Guluzar
dc.contributor.authorHooshiar, Mohammad Hosseini
dc.contributor.authorShirazi, Malihe Sagheb Ray
dc.contributor.authorDogus, Yusuf
dc.date.accessioned2026-04-25T14:19:47Z
dc.date.available2026-04-25T14:19:47Z
dc.date.issued2025
dc.departmentSinop Üniversitesi
dc.description.abstractCRISPR-based epigenetic editing enables reversible regulation of gene expression without permanent DNA modification. The integration of artificial intelligence (AI) enhances guide RNA (gRNA) design, off-target prediction, and delivery optimization. We conducted a systematic review and meta-analysis (2015-2025) in accordance with PRISMA 2020 guidelines to evaluate the impact of AI on the precision, safety, and therapeutic efficacy of epigenetic CRISPR tools. From 540 screened records, 58 studies met inclusion criteria, of which 41 provided extractable quantitative data for meta-analysis and 17 contributed to qualitative synthesis. Random-effects models, subgroup analyses, and bias assessments were applied. Pooled analyses demonstrated strong positive effects across three domains: therapeutic efficacy (SMD = 1.67), gRNA optimization (SMD = 1.44), and off-target prediction (AUC = 0.79). Publication bias was minimal, and subgroup analyses indicated the strongest impact in therapeutic applications. Deep learning models were consistently associated with higher effect sizes. Qualitative synthesis revealed trends in interpretable AI, omics integration, and delivery innovations, underscoring AI's role in safer and more precise CRISPR editing. Overall, AI significantly improves the precision and therapeutic performance of CRISPR-based epigenetic tools, with the strongest effects observed in therapeutic efficacy, supporting their potential for personalized gene editing.
dc.identifier.doi10.1007/s10142-025-01725-8
dc.identifier.issn1438-793X
dc.identifier.issn1438-7948
dc.identifier.issue1
dc.identifier.orcid0000-0003-1104-4626
dc.identifier.pmid41136797
dc.identifier.scopus2-s2.0-105019551965
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1007/s10142-025-01725-8
dc.identifier.urihttps://hdl.handle.net/11486/8188
dc.identifier.volume25
dc.identifier.wosWOS:001600910000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofFunctional & Integrative Genomics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260420
dc.subjectCRISPR
dc.subjectArtificial intelligence
dc.subjectEpigenetic editing
dc.subjectGRNA optimization
dc.subjectOff-Target prediction
dc.titleArtificial intelligence-driven epigenetic CRISPR therapeutics: a structured multi-domain meta-analysis of therapeutic efficacy, off-target prediction, and gRNA optimization
dc.typeArticle

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