Research Progress in Genome Sequencing and Functional Gene Mining of Cannabis
Bibliographic record
Abstract
The primary goal of this study is to advance the understanding of the Cannabis sativa genome and to identify functional genes that contribute to its medicinal, industrial, and agricultural applications. Our comprehensive analysis revealed several key findings. Current Cannabis genome assemblies are incomplete, with significant portions missing or unmapped, which hampers accurate gene annotation. Recent advancements in genomics have identified four genes significantly associated with lifetime cannabis use: NCAM1, CADM2, SCOC, and KCNT2, which are linked to various phenotypes such as substance use and body mass index. Additionally, a high-quality reference genome for wild Cannabis sativa has been developed, providing valuable genetic resources for future research. In silico approaches have been proposed for genome editing, targeting genes involved in cannabinoid biosynthesis, which could lead to novel applications in agriculture and medicine. Furthermore, virus-induced gene silencing (VIGS) methods have been successfully applied to study gene functions in cannabis, demonstrating the potential for functional gene studies. The findings underscore the importance of coordinated efforts to complete and refine Cannabis genome assemblies. The identification of key genes and the development of advanced genomics tools hold significant promise for the genetic improvement of cannabis. These advancements could lead to enhanced medicinal and industrial applications, ultimately benefiting various sectors including agriculture, pharmaceuticals, and biotechnology.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".