GENOME-WIDE IDENTIFICATION OF NICOTIANA TABACUM MIRNAS AND THEIR ROLE IN HUMAN HEALTH – A COMPUTATIONAL GENOMICS ASSESSMENT
Bibliographic record
Abstract
Tobacco kills their half of the consumers still, grown as the most lucrative crop worldwide for different uses like chewing, smoking, and snuffing. On the other side, it was known as a chief medicinal plant by Native Americans, ancient European, Amazonian and Indian. In the middle 20th century tobacco was declared a negative and hazardous plant because of its nicotine component. Nicotiana tabacum (N. tabacum) is well studied in plant biotechnology and studied as a model plant. Likewise. N. tabacum miRNAs were also identified a decade ago however a genome-wide computational approach to identify miRNAs remained to be explored. These XenomiRs and their cross-species talk were also revealed for the first time in the current study. The top ten hub nodes (CCNE1, DDX5, NEUROD1, SOS1, CUL2, OPHN1, SOX9, KCNA1, FBXW2, and NOC3L) were retrieved from the experiment which gives evidence of tobacco miRNAs and their involvement in diseases like carcinoma and neurodevelopmental disorders.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".