TripLexicon, a database of predicted gene regulatory RNA-DNA interactions
Bibliographic record
Abstract
This repository contains all files, databases, plots and code used to generate the TripLexicon Webserver. Description of files: TripLexicon-main.zip : contains the github repository with the source code of TripLexicon as well as the documentation with readthedocs. Triplexicon_plots_15_09_25.zip: contains all plots used by the TripLexicon webserver databases_16_09_25.zip: contains the two databases (mouse and human) with the predicted triplexes by TriplexAligner which can be queried and visualized via TripLexicon. tables_17_05_25.zip: tables with the data contained in the two TripLexicon databases (mouse and human) bed_files_DNA_regions_16_09_25.zip: BED files containing all DNA target regions involved in triplex formation as given in TripLexicon beds_TripLexicon_17_09_25.zip: BED files of DNA regions bound per RNA, used for visualization in the UCSC genome browser
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.089 |
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".