Abstract A045: Development of automated deep learning-based off-target distribution prediction system for CRISPR-Cas13 system
Bibliographic record
Abstract
Abstract CRISPR-Cas13 RNA editing system is a new member of the CRISPR-Cas gene editing system family, which can perform guided editing on RNA sequence. CRISPR-Cas gene editing system is currently one of the breakthrough technologies in life science, which is easy to operate, has a high editing efficiency and theoretically can perform guided editing on genome of any species. CRISPR-Cas9 system is the most commonly used CRISPR-Cas system, but it is mainly applied to DNA. Hence, fornon-destructive gene therapy and RNA virus detection, CRISPR-Cas13 system has its unique advantage. However, there are severe issues limiting its application, the most important one of which is off-target effect, namely the guide RNA points system to the wrong position causing non-targeted region edited, which leading to wrong expression spectrum of the cell. Therefore, based on automated deep learning and transfer learning techniques, this project aims to establishing an off-target distribution prediction system of CRISPR-Cas13 RNA editing system, in order to make high-specificity RNA editing possible. Citation Format: Guohui Chuai, Yanjing Zhu. Development of automated deep learning-based off-target distribution prediction system for CRISPR-Cas13 system [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A045.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".