Protein-Protein Interaction Networks in Rice under Drought Stress: Insights from Proteomics and Bioinformatics Analysis
Bibliographic record
Abstract
This review outlines the physiological and biochemical responses of plants to drought stress, explains the molecular mechanisms, and emphasizes the key role of proteomics in these responses. Drought stress causes dehydration and osmotic changes in plants, leading to cell membrane damage, accumulation of reactive oxygen species (ROS), and metabolic disorders. Plants respond to drought stress through a series of complex physiological and biochemical responses, including regulate of stomatal opening and closing, synthesis protective proteins and metabolites, activate antioxidant systems, and regulate gene expression. Through proteomic and bioinformatic analysis, we systematically synthesis findings that identified key response proteins in rice under drought stress, constructed and analyzed the PPI network, performed functional annotation and pathway enrichment analysis, and demonstrated specific PPI networks involving transcription factors and signaling proteins, interaction networks with osmoprotectants and stress-related proteins, and comparative analysis of PPI networks of different rice varieties under drought stress through case studies. By exploring the response mechanism of rice under drought stress, we propose to develop more effective drought resistance strategies to improve the stability and sustainability of rice production.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".