AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing
Bibliographic record
Abstract
The advent of artificial intelligence (AI) holds great promise for revolutionizing the fields of plant tissue culture and genome editing. Plant tissue culture is recognized as a powerful tool for rapid multiplication and crop improvement. However, the complex interactions between genetic and environmental factors generate large volumes of data, posing challenges for traditional statistical analysis methods. To address this, researchers are now employing machine learning (ML)-based and artificial neural networks (ANN) approaches to predict and optimize in vitro culture protocols thereby improving precision, sustainability, and efficiency. Integrating AI technologies such as machine learning (ML), artificial neural networks (ANN), and deep learning (DL) can significantly advance the development of data-driven models for CRISPR/Cas9 genome editing. Today, AI-driven methods are routinely applied to enhance precision in predicting on- and off-target sequence locations and editing outcomes. Additionally, predicting protein structures can provide a directed evolution framework that facilitates the creation of improved gene editing tools. However, the application of AI-based CRISPR modeling in plants is not yet fully explored. In this context, we aim to examine representative ML/DL/ANN models of CRISPR/Cas based editing employed in various organisms. This review significantly compiles a diverse set of studies and provides a clear overview of how AI is transforming the fields of plant tissue culture and genome editing. It emphasizes AI’s potential to increase the efficiency and precision of biotechnological practices, making them more accessible and cost-effective. While outlining current findings, the paper sets the stage for future research, encouraging further exploration into the integration of AI with plant biotechnology.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".