ArticleMethods in molecular biology (Clifton, N.J.)2025
Artificial Intelligence-Based Genome Editing in CRISPR/Cas9.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Advanced gene editing technologies for oncology mechanisms, applications, and clinical implementation.Cancer gene therapy · 2026Review
- The future of pediatric gene therapy: CRISPR-Cas9, AI, and personalized medicine.Pediatric research · 2026Review
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Contribution and Regulation of HIF-1α in Testicular Injury Induced by Diabetes Mellitus.Biomolecules · 2025Review
- Precision Neuro-Oncology in Glioblastoma: AI-Guided CRISPR Editing and Real-Time Multi-Omics for Genomic Brain Surgery.International journal of molecular sciences · 2025Review
- Applications of artificial intelligence in cancer immunotherapy: a frontier review on enhancing treatment efficacy and safety.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) plays a critical role in predicting and improving genome editing methods, including CRISPR/Cas9. Recently, several AI models, such as DeepCRISPR, CRISTA, and Deep High Fidelity (DeepHF), have been utilized to design guide RNAs (gRNAs) for CRISPR-Cas systems. These models assess genomic context, desired mutation type, on-target and off-target scores, and potential off-target locations. AI models help improve various genome editing methods, such as base, prime, and epigenome editing, which allow for precise and intentional changes to DNA sequences without the need for donor DNA templates. Furthermore, integrating AI with genome editing and precision medicine enables the creation of personalized treatments tailored to each individual's unique genetic profile. Examining genomic data enables the identification of mutations, variations, and biomarkers linked to diseases like cancer, diabetes, and Alzheimer's disease. Integrating AI with genome editing can potentially enhance genetic modification techniques' precision, efficiency, and cost-effectiveness. Furthermore, it presents novel prospects for fields like genetics, biomedicine, and healthcare, which could significantly impact human health. However, several challenges still exist, including high costs, inaccurate edits, and effective delivery methods for CRISPR components, improved editing performance, and safety in clinical applications.
Indexed as
Identifiers
40553339What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.