Identification of conserved microRNAs and their targets in Chinese cabbage (<i>Brassica rapa</i> subsp. <i>pekinensis</i>)
Bibliographic record
Abstract
The microRNAs (miRNAs) are a new class of small nonprotein-coding RNAs that have been identified to regulate gene expression at the post-transcriptional level by targeting mRNAs for degradation or by inhibiting protein translation. Until now, thousands of miRNAs have been identified in many plants species. However, only 23 miRNAs have been reported from the microRNA database in Chinese cabbage (Brassica rapa subsp. pekinensis), one of the most widely cultivated vegetables in China and East Asia. In the present study, 168 potential miRNAs, derived from 22 EST and 119 GSS sequences in Chinese cabbage were identified and classified into 38 miRNA families by well-defined computational analysis, in which most belonged to the miRNA1533, miRNA156, and miRNA2911 families. Totally, there are 129 identified miRNAs potentially targeting 1386 Chinese cabbage EST genes, which play roles in multiple biological and metabolic processes including metabolism, cell growth, signal transduction, stress response, and plant development. Gene ontology analysis, based on these target proteins, showed that 688, 532, and 287 genes were involved in molecular functions, biological processes, and cellular components, respectively. KEGG pathway analysis demonstrated that these miRNAs participated in 214 metabolism pathways, including, amongst others, plant-pathogen interaction, fatty acid metabolism, amino acid metabolism, nitrogen metabolism, plant hormone signal transduction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".