Identification of Groundnut miRNA and their targets
Bibliographic record
Abstract
MicroRNA (miRNA) are ~22nt small non-coding RNA molecules which play an important role in post-transcriptional gene regulation in both plants and animals. As the miRNAs are highly conserved among species, comparative genomics based homology search has played a key role in identifying new miRNAs in different species whose genomes are not yet sequenced. Arachis hypogaea (groundnut or peanut) is one such legume crop and being grown in more than 100 countries, ranks third worldwide among oilseeds produced. In India, it is the second largest in terms of production but stands first in terms of area of cultivation. Identifying miRNAs and their targets can be helpful in crop improvement. In the present study, we tried to identify new conserved miRNA from the 205442 ESTs through blast search, using previously known plant miRNAs. The non-protein coding sequences with homology showing no more than 3 mismatches were folded back to stem-loop structure. These were subsequently passed through strict filtration criteria to obtain new miRNAs belonging to different miRNA families, as well as their targets.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".