In silico identification and characterization of microRNAs and their putative target genes in <i>Solanaceae</i> plants
Bibliographic record
Abstract
MicroRNAs (miRNAs) are a class of small, single-stranded, noncoding RNAs ranging from 19 to 25 nucleotides. The miRNA control various cellular functions by negatively regulating gene expression at the post-transcriptional level. The miRNA regulation over their target genes has a central role in regulating plant growth and development; however, only a few reports have been published on the function of miRNAs in the family Solanaceae. We identified Solanaceae miRNAs and their target genes by analyzing expressed sequence tag (EST) data from five different Solanaceae species. A comprehensive bioinformatic analysis of EST data of Solanaceae species revealed the presence of at least 11 miRNAs and 54 target genes in pepper (Capsicum annuum L.), 22 miRNAs and 221 target genes in potato (Solanum tuberosum L.), 12 miRNAs and 417 target genes in tomato (Solanum lycopersicum L.), 46 miRNAs and 60 target genes in tobacco (Nicotiana tabacum L.), and 7 miRNAs and 28 target genes in Nicotiana benthamiana. The identified Solanaceae miRNAs and their target genes were deposited in the SolmiRNA database, which is freely available for academic research only at http://genepool.kribb.re.kr/SolmiRNA. Our data indicate that the Solanaceae family has both conserved and specific miRNAs and that their target genes may play important roles in growth and development of Solanaceae plants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".