Bibliographic record
Abstract
This study presents a new application of TOPSIS for the optimization of transcription factors characteristics. This application is essential as it can help compare the characteristics of these proteins and determine the optimized output of their comparison with this decision-making method. The hypothesis in this article was that according to the previous study of the Cdx transcription factors, as the Cdx2 transcription factor showed more robust characteristics than Cdx1 and Cdx4, the TOPSIS method would show a better rank position of these first proteins in comparison with the two other ones. Moreover, the engrailed repressor domain EnRCdx1 used in the plasmid showed the reduction of the pax3 gene expression in comparison with the induced regulation of the gene expression with the production of the Cdx1, Cdx2, and Cdx4 transcription factors using the corresponding plasmids, the worst rank position with TOPSIS was expected for this repressor domain. The results obtained with this ranking method showed that the rank positions of the transcription factors and the repressor domain corresponded to their compared properties. Moreover, the change in the weight values of the candidates showed the modification of their distances from the best and worst alternatives and closeness coefficients. However, as expected, the candidates’ rank positions were unchanged, and the Cdx2 transcription factor was still the best candidate. The results of this article can be used in computer engineering to improve biological applications of these proteins.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".