Integrative omics approaches for enhancing abiotic stress resilience in maize
Bibliographic record
Abstract
Abiotic stresses such as drought, heat, salinity, and nutrient imbalances severely threaten maize productivity, necessitating innovative strategies for developing resilient cultivars. Omics technologies have emerged as transformative tools to unravel the complex molecular and physiological mechanisms. Each omics layer, genomics, transcriptomics, proteomics, metabolomics, ionomics, and phenomics, provides unique insights into how maize perceives and adapts to adverse environments. When integrated, these approaches generate a systems-level perspective that connects molecular signals with biochemical pathways, physiological responses, and morphological traits, thereby advancing our understanding of resilience at multiple biological scales. Integrating multi-omics with high-throughput phenotyping has accelerated the identification of biomarkers, regulatory networks, and candidate genes associated with stress tolerance. Significantly, omics-driven approaches facilitate the development of climate-smart cultivars capable of sustaining yield stability under fluctuating and extreme conditions. Future studies will depend on coupling omics with advanced analytics, machine learning, and environmental datasets to strengthen predictive capacity. Emerging innovations, including field-deployable omics platforms and AI-integrated decision-support tools, hold promise for real-time trait selection and adaptive management strategies. Ultimately, the successful translation of omics-derived knowledge into breeding programs will require global collaboration, open-access databases, and integration into precision agriculture frameworks. Such efforts will help ensure food security, resource-use efficiency, and sustainable maize production in a changing climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".