Research on Structural Variation Detection Methods of Wheat Genome Based on Deep Learning
Bibliographic record
Abstract
Due to the complexity of genome structure and technical conditions, wheat genome structure variation has not yet been comprehensively and accurately detected and evaluated for genetic effects.The aim of this study is to construct a method based on deep learning algorithm to accurately detect genomic structure variation in wheat.The method converts genomic data into image form by genomic structure variation image generation algorithm.A gene structure variation prediction model is constructed based on deep learning, and efficient and accurate structure variation prediction is realized by automatically extracting and analyzing the variation features in the image.The experimental results show that this method has better detection performance than other structural variation detection methods based on third-generation sequencing data, especially in the structural variation detection of the "Sequencing and Assembly of Spring Wheat Genome in China" project, and the accuracy, precision, and recall rate of this method are all over 90%.This study provides a novel deep learning framework for efficiently detecting structural variants in the wheat genome, and provides powerful technical support for genetic improvement and breeding research of wheat.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".