Artificial intelligence-driven epigenetic CRISPR therapeutics: a structured multi-domain meta-analysis of therapeutic efficacy, off-target prediction, and gRNA optimization (Retracted article. See vol. 26, 2026)
Yazarlar (7)
Prof. Dr. Mustafa Kemal Basarali Dicle University, Faculty of Medicine, Türkiye
Amin Daemi
Hormozgan University of Medical Sciences
Hormozgan University of Medical Sciences
Ruhiyya Guliyeva Tahiraga
Institute of Biophysics Ministry of Science And Education Republic of Azerbaijan, Azerbaycan
Institute of Biophysics Ministry of Science And Education Republic of Azerbaijan, Azerbaycan
Doç. Dr. Gülüzar ÖZBOLAT Sinop Üniversitesi, Türkiye
Mohammad Hosseini Hooshiar
School of Dentistry
School of Dentistry
Malihe Sagheb Ray Shirazi
Hormozgan University of Medical Sciences
Hormozgan University of Medical Sciences
Arş. Gör. Yusuf Dogus Hormozgan University of Medical Sciences
| Makale Türü |
Diğer (Teknik, not, yorum, vaka takdimi, editöre mektup, özet, kitap krıtiği,
araştırma notu, bilirkişi raporu ve benzeri)
(SCI, SSCI, AHCI, SCI-Exp dergilerinde yayınlanan teknik not,
editöre mektup, tartışma, vaka takdimi ve özet türünden makale)
|
||
| Dergi Adı | Functional and Integrative Genomics (Q2) | ||
| Dergi ISSN | 1438-793X Dergi Bilgileri (2025) | ||
| Makale Dili | İngilizce | Basım Tarihi | 10-2025 |
| Kabul Tarihi | 30-09-2025 | Yayınlanma Tarihi | 25-10-2025 |
| Cilt / Sayı / Sayfa | 25 / 1 / – | DOI | 10.1007/s10142-025-01725-8 |
| Makale Linki | https://link.springer.com/10.1007/s10142-025-01725-8 | ||
| UAK Araştırma Alanları |
|
||
| Özet |
| CRISPR-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. |
| Anahtar Kelimeler |
| Artificial intelligence | CRISPR | Epigenetic editing | GRNA optimization | Off-Target prediction |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Scopus | 1 |