On the characterization of protein-DNA interactions using statistical potentials and protein-protein interactions
Bibliographic record
Abstract
Protein-DNA interactions are indispensable players in the daily activities of cells. DNA-binding proteins regulate gene expression and are responsible of DNA replication, packaging, repair and recombination. Among them, transcription factors activate/repress gene transcription by binding to specific genomic sites. Hence, the characterization of transcription factor binding sites turns out to be crucial in order to understand gene regulation. In this context, the development of computational tools is foremost. Here, I show the prediction of redundant transcription factors in yeast using a combination of homology-based tools and protein-protein interactions. The approach was automated and incorporated into ModLink+, an online and user-friendly tool to infer the fold of remote homologs. Moreover, I describe split-statistical potentials for protein-DNA interactions. Finally, I present SHAITAN, a statistical/homology-based approach that can be used to both predict transcription factor binding sites and infer the more likely transcription factors to bind a DNA sequence of interest.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".