Embracing the Omics Era for Plant Breeding
Bibliographic record
Abstract
The increasing demand for food, feed, fuel, and fiber in modern society calls for urgent crop improvement, especially when faced with challenges such as climate change and decreasing arable land. Therefore, there is a constant need for advances in plant breeding. Over the last two decades, high-throughput techniques, such as next-generation sequencing, have given momentum to multiple omics technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, generating an immense amount of data daily. These technologies and advanced bioinformatic tools enhance our understanding of agronomically important traits, including yield, nutrient content, and tolerance to biotic/abiotic stresses. For example, research on nucleotide-binding leucine-rich-repeat (NLR) genes, key players in plant immunity, is driven by high-throughput gene discovery, functional annotation, and synthetic design, accelerating disease resistance breeding. Additionally, high-throughput techniques facilitate the generation of valuable tools like molecular markers, which can be utilized in applications such as marker-assisted selection, quantitative trait locus mapping, genome-wide association study, and genomic selection. As regulations on genetically engineered crops move from process-based approaches toward product-based approaches, omics technologies are expected to play a pivotal role in regulating new crop varieties by assessing substantial equivalence. Embracing the omics era in plant breeding requires a paradigm shift in every aspect of the field, and readiness is essential.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".