Genomic Strategies for Disease Resistance Breeding in Sugarcane: Identification of Resistance Genes, Transcriptomic Analysis, and Molecular Markers
Bibliographic record
Abstract
Sugar cane is an important sugar crop in the world. Its yield and quality are often affected by a variety of diseases, which seriously restricts industrial development. Traditional disease-resistant breeding methods have problems such as long cycles and low efficiency, which are difficult to meet the needs of modern agriculture for efficient and precise breeding. With the advancement of genomics technology, sugarcane disease-resistant breeding has entered a new stage. This study systematically reviews the genomic strategies of sugarcane disease-resistant breeding, covering genomic resource construction, disease-resistant gene mining and functional verification, transcriptome analysis, molecular marker development, and multiomic integration application. Particularly emphasized the potential of emerging technologies such as gene editing, pan-genome and artificial intelligence in improving the efficiency of disease-resistant breeding. By integrating multi-level omics data and advanced technologies, sugarcane disease-resistant breeding is expected to achieve the transformation from traditional experience to precise design, providing a solid theoretical foundation and technical support for cultivating new sugarcane varieties with high yield, high sugar and disease-resistant sugarcane.
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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 | 0.001 |
| 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".