Bibliographic record
Abstract
Introduction: CRISPR-Cas9, a ground breaking gene editing tool, has transformed genetic engineering. With the advent of Artificial intelligence (AI) in recent years, this tool has been applied to the development of new CRISPR-Cas9 therapies. This integration with artificial intelligence (AI) “Helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases” Methods: A comprehensive literature review was conducted, with the assistance of AI to compile and assess the current state and future prospects of integrating AI with CRISPR-Cas9 technology in genomic medicine. Relevant articles were identified through database searches in PubMed, Web of Science, and Google Scholar, using keywords such as "CRISPR-Cas9," "artificial intelligence," "machine learning," "gene editing," and "genomic medicine." Studies published up to June 2024 were considered. Current Research and Findings: Current research revolves around the development of CRISPR systems with improved efficiency and specificity, facilitated by advanced machine learning models. These focus specifically on sgRNA development and its consequences on genetic research. New Research and Implications on Future Directions: New research avenues suggest exploring CRISPR’s use in complex genetic disorders involving multiple genes. AI's predictive capabilities are vital in designing multi-target strategies for such complex conditions. These include the diagnosis and treatment of cancer, the early identification of rare diseases, and the faster design of vaccines. Summary: The convergence of these advanced technologies offers a pathway to more precise and personalized therapeutic interventions. By leveraging AI's capabilities in data analysis and pattern recognition, researchers can enhance the accuracy and efficiency of CRISPR-Cas9 gene editing. AI aids in predicting the most effective guide RNA (gRNA) designs, reducing off-target effects, and improving the specificity of gene edits. This synergy not only accelerates the gene editing process but also expands its applications across various medical fields. The ongoing refinement of AI algorithms and their integration with CRISPR technology could unlock new possibilities in medical science, potentially revolutionizing the diagnosis and treatment of genetic and multifactorial diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".