Bibliographic record
Abstract
The rapid development of -omics techniques have provided an unprecedented amount of data, enabling system-wide biological research. However, the success of systems biology is contingent on the ability to integrate a wide variety of types of biological data to automatically predict, assign functional annotations of proteins and perform comparative analyses. Although each biological data integration system presents to some extent a number of desirable features, none of them meets all the requirements for effective integration of system-wide data. In this paper, we present BenchDW, a generic and flexible benchmark framework that aims at facilitating the evaluation and quantification of the capabilities of those biological data warehouses. It currently comprises 22 different metrics ranging from documentation quality to accuracy and response times, which may be recorded for different hardware configurations. Each metric can be weighted to better suit the user's specific needs and compared to the gold standard. BenchDW was designed to be flexible, easy to use and offers many benefits over spreadsheets, thus presenting the characteristics required to facilitate acceptance by the scientific community. We demonstrate the utility of BenchDW by briefly reviewing three data warehouses (BioMart, BioXRT and InterMine) and by showcasing how it can be leveraged to identify the specificities of the systems of interest. BenchDW is available online at http://warehousebenchmark.fungalgenomics.ca/benchmark/benchdw/index.html under the GNU GPLv3 license.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.042 |
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".