Cluster Analyzer for Transcription Sites (CATS): a C++-based program for identifying clustered transcription factor binding sites
Bibliographic record
Abstract
SUMMARY: We have developed a program, Cluster Analyzer for Transcription Sites (CATS), which identifies clusters of transcription factor binding sites in any genome sequence. The program searches for clusters of the consensus sequence for DNA binding within a window (length of DNA). The window size and the cluster size (number of consensus sequences within a given window) can be varied. CATS can be used for single or multiple transcription factors for which consensus sequences have been deduced based on biochemical and mutational analysis, or by comparative genomics. The use of CATS for clusters of different transcription factor binding sites may facilitate the identification of genes that are co-regulated in a cell type-specific or developmental stage-specific manner. CATS is simple to install and use on computers running any Windows NT-platforms. AVAILABILITY: http://www.healthsciences.columbia.edu/dept/greenwaldlab/links.html
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.024 |
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".