Editorial: Structural bioinformatics and biophysical approaches for understanding the plant responses to biotic and abiotic stresses
Bibliographic record
Abstract
Plants are exposed to a variety of environmental conditions that negatively impact \n their physiology and yields (Rivero et al., 2022; Sharma et al., 2022; Zandalinas and \n Mittler, 2022). Several studies identified mechanisms involving genes, proteins, and \n metabolites that underlie plant responses to stress conditions (Chen et al., 2022; Hassan \n et al., 2022; Huang et al., 2022; Mittler et al., 2022; Yang et al., 2022; Zhan et al., \n 2022). Some of these molecules were used to improve plant responses to abiotic \n and biotic stresses (Li et al., 2022; Mahto et al., 2022; Wang and Komatsu, 2022; \n Zhao et al., 2022). However, the underlying structural and functional relationships of \n these molecular mechanisms require more research. Computational and biophysical \n approaches are viable options for analyses of target molecules to understand their \n interactions and dynamics that initiate biochemical and physiological responses of \n plants (Wan et al., 2015; Konda et al., 2018; Moffett and Shukla, 2018; Rayevsky et al., \n 2019; Jha et al., 2022). These responses in turn control plant tolerance and resistance \n to sub-optimal environmental conditions. Therefore, this Research Topic is aligned \n with the current research trends and provides an update on the advances in structural \n bioinformatics and biophysical approaches to understanding plant responses to biotic \n and abiotic stresses at the molecular level. Here, we highlight some of the topics from the \n following contributions.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.030 | 0.020 |
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".