A data management system for structural genomics
Bibliographic record
Abstract
Background: Structural genomics (SG) projects aim to determine thousands of protein structures by the development of high-throughput techniques for all steps of the experimental structure determination pipeline.Crucial to the success of such endeavours is the careful tracking and archiving of experimental and external data on protein targets. Results:We have developed a sophisticated data management system for structural genomics.Central to the system is an Oracle-based, SQL-interfaced database.The database schema deals with all facets of the structure determination process, from target selection to data deposition.Users access the database via any web browser.Experimental data is input by users with predefined web forms.Data can be displayed according to numerous criteria.A list of all current target proteins can be viewed, with links for each target to associated entries in external databases.To avoid unnecessary work on targets, our data management system matches protein sequences weekly using BLAST to entries in the Protein Data Bank and to targets of other SG centers worldwide.Conclusion: Our system is a working, effective and user-friendly data management tool for structural genomics projects.In this report we present a detailed summary of the various capabilities of the system, using real target data as examples, and indicate our plans for future enhancements.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.080 | 0.110 |
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".