CRISPR as a Versatile Technology for Gene Activation and Genome Editing
Bibliographic record
Abstract
CRISPR/Cas system, a microbial adaptive immune system, has rapidly transformed the ways researchers can interrogate the genome. CRISPR has many advantages over traditional methods such as Transcription activator-like effector nucleases (TALEN) and Zinc-finger nucleases (ZFN). Since CRISPR discovery as an adaptive immune system used by bacterial against viruses, it has been repurposed to help in many different genome-related studies such as gene knocking in and out, gene expression upregulation and downregulation. Also CRISPR holds vast therapeutic potential for the management of genetic disorders by straight modifying disease-causing mutations. Although the Cas9 protein has been revealed to attach and cleave DNA at off-target sites, the field of Cas9 specificity is quickly progressing, with marked modifying in guide RNA choice, protein and guide engineering, innovative enzymes, and off-target recognition methods. In current review we mostly focus on CRISPR unique ability in gene activation/ upregulation, which has wide applications in different aspects such as gene studies, stem cell differentiation, and trans-differentiation. Compared to other gene activation methods such as viral gene overexpression, TALEN and ZFN, CRISPR offers many benefits such as easy designing and high precision.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".