Conservation analysis of potential cis-NATs in Brassicaceae plants for crop improvement
Bibliographic record
Abstract
Canola fuels a multi-billion dollar industry in Canada. It is a Canadian trademarked name of specific cultivars derived from specific Brassicaceae plants. Cis-NATs are natural antisense transcripts that overlap a gene and are not translated into proteins. Instead, they silence their parent gene's expression through various mechanisms. Their role in humans is well established, but their role in plants is relatively obscure. The goal of this thesis project is to analyze the conservation of cis-NATs across 8 different Brassicaceae genera (9 species). This is useful for picking up targets for crop improvement in canola. Conservation was studied across the 9 species, then across two subgroups of 4 and 2 species, respectively; cis-NATs simultaneously exhibiting conservation in all three scenarios were selected. A total of 34 potential candidates were identified. The study also suggests that the type of a cis-NAT might also affect its conservation. The presented methodology is a powerful pre-screening strategy to direct experimental efforts. It can be used with genes and other transcribed non-coding DNA.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".