Analysis of gene expression data in transgenic and non- transgenic soybean cultivars using bioinformatics tools
Bibliographic record
Abstract
Current safety assessment for novel crops, including transgenic crops, uses a targeted approach, which determines crop safeness by assessing the content of a few specific chemical components. However, microarray technology can simultaneously assess the whole transcriptome and can therefore be used to analyze target genes as well as unintended effects. In this study, we used this technique as a non-targeted approach. Gene expression data from a microarray experiment with five soybean cultivars was analyzed using bioinformatics. Two cultivars were transgenic (RoundUp®) and three were non-transgenic. We show that the variation in gene expression between transgenic and non-transgenic soybean is less than that between non-transgenic cultivars. A MySQL database coupled with CGI web interfaces was developed to store and present the results (http://thor.agrenv.mcgill.ca/cgi-bin/soy/soybean.cgi). By integrating the microarray data with gene annotations and other soybean data, a comprehensive view of differences in gene expression can be explored between cultivars.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".