Bibliographic record
Abstract
Abstract 0.1 Motivation Potential transcription factor (TF) complexes may be identified by testing whether the binding sequences of individual TF proteins form clusters with each other. These clusters may also indicate TF inhibition due to competitive occupancy of enhancer regions. Genome annotation data containing the coordinates of enhancer sequences is highly accessible via position-weight matrix tools. 0.2 Results An algorithm called CCSeq (Clusters of Colocalized Sequences) was developed for identifying clusters of sequences along a one-dimensional line, such as a chromosome, given genome annotation files and a cut-off distance as inputs. The algorithm was applied to the binding sequences of the constituent proteins of two known transcription factor complexes, the HSF1 homotrimer and one form of the NF- κ B complex, a dimer of NFKB2 and RELB. 28 clusters of HSF1 trimer binding sequences were identified on chromosome Y, and 16 clusters of the NFKB2 and RELB dimer were identified on chromosome 17, compared to 0 clusters identified in any of the five simulated random distributions for each of the two sets of TF proteins. Additionally, structural patterns of these binding sequence clusters are described. 0.3 Availability and Implementation This algorithm is freely available as an R package on the open source R repository CRAN at the following link: https://cran.r-project.org/package=colocalized . Genome annotation files were obtained from the PWMScan tool at https://ccg.epfl.ch/pwmtools/pwmscan.php hosted by the Swiss Insitute of Bioinformatics (2) (3).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".