GAIA: G-quadruplexes in alive creature database
Bibliographic record
Abstract
G-quadruplexes (G4) are 3D structures that are found in both DNA and RNA. Interest in this structure has grown over the past few years due to both its implication in diverse biological mechanisms and its potential use as a therapeutic target, to name two examples. G4s in humans have been widely studied; however, the level of their study in other species remains relatively minimal. That said, progress in this field has resulted in the prediction of G4s structures in various species, ranging from bacteria to eukaryotes. These predictions were analysed in a previous study which revealed that G4s are present in all living kingdoms. To date, eleven different databases have grouped the various G4s depending on either their structures, on the proteins that might bind them, or on their location in the various genomes. However, none of these databases contains information on their location in the transcriptome of many of the implicated species. The GAIA database was designed so as to make this data available online in a user-friendly manner. Through its web interface, users can query GAIA to filter G4s, which, we hope, will help the research in this field. GAIA is available at: https://gaia.cobius.usherbrooke.ca.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.046 |
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".