Mapping Transcription Factors from a Model to a Non-model Organism
Bibliographic record
Abstract
Identification of regulatory elements, such as transcription factors, is useful in construction of regulatory networks and to understand gene regulation. These transcription factors have already been recognized for model organisms based on extensive experiments but have not been as heavily investigated for non-model organisms. This paper proposes to use Basic Local Alignment Search Tool (BLAST) to map the transcription factors from a model to a non-model organism. Experiments are performed on bacterial organisms based on evolutionary distance to compare the results. Analysis of the results suggest that transcription factors can be mapped from one bacterial organism to another as transcription factor motifs are well preserved among these organisms. Results are also analyzed to determine the best suitable threshold for the e-value parameter of BLAST that can be used to map transcription factors, determine to be the e-value thresholds of 0.01 and 0.1. Both the BLAST e-value threshold and evolutionary distance from the model organism used for mapping have significant impact on the quality of results.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".