Seahawk: moving beyond HTML in Web-based bioinformatics analysis
Bibliographic record
Abstract
BACKGROUND: Traditional HTML interfaces for input to and output from Bioinformatics analysis on the Web are highly variable in style, content and data formats. Combining multiple analyses can therefore be an onerous task for biologists. Semantic Web Services allow automated discovery of conceptual links between remote data analysis servers. A shared data ontology and service discovery/execution framework is particularly attractive in Bioinformatics, where data and services are often both disparate and distributed. Instead of biologists copying, pasting and reformatting data between various Web sites, Semantic Web Service protocols such as MOBY-S hold out the promise of seamlessly integrating multi-step analysis. RESULTS: We have developed a program (Seahawk) that allows biologists to intuitively and seamlessly chain together Web Services using a data-centric, rather than the customary service-centric approach. The approach is illustrated with a ferredoxin mutation analysis. Seahawk concentrates on lowering entry barriers for biologists: no prior knowledge of the data ontology, or relevant services is required. In stark contrast to other MOBY-S clients, in Seahawk users simply load Web pages and text files they already work with. Underlying the familiar Web-browser interaction is an XML data engine based on extensible XSLT style sheets, regular expressions, and XPath statements which import existing user data into the MOBY-S format. CONCLUSION: As an easily accessible applet, Seahawk moves beyond standard Web browser interaction, providing mechanisms for the biologist to concentrate on the analytical task rather than on the technical details of data formats and Web forms. As the MOBY-S protocol nears a 1.0 specification, we expect more biologists to adopt these new semantic-oriented ways of doing Web-based analysis, which empower them to do more complicated, ad hoc analysis workflow creation without the assistance of a programmer.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".