DECIPHERING THE ROLE OF EXPERIMENTALLY VALIDATED NICOTIANA TABACUM (TOBACCO) MIRNAS IN HUMAN HEALTH – A COMPUTATIONAL GENOMICS ASSESSMENT
Bibliographic record
Abstract
Tobacco (Nicotiana tabacum) is considered as the tropical model plant for research especially for alkaloid like nicotine. One of the public health problems worldwide is harmful usage of tobacco that kills half of their consumers. On the other hand, Nicotiana tabacum was used as chief medicinal plants by native Americans, Amazonian and ancient Indians to cure poisonous reptiles’ bites and multiple diseases. MicroRNA (miRNA) is a prime gene regulator amongst the class of small-RNAs which binds with mRNA using translational repression or cleavage mechanism. Till the date, tobacco plant derived miRNAs were studied to check stress response in different biotic and abiotic condition and phylogenetic analysis, plant growth and development. Thus, cross-kingdom approach helps to understand the possible regulation as well as modulation in human health targeted by tobacco specific miRNAs. Tobacco derived miRNAs along with their targets were predicted and functionally annotated, pathway enrichment and disease association were studied in this study. Conclusively, we can report that N. tabacum miRNAs showed association with carcinoma and multiple neural, cardiac disorders.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".