Michela<scp>Ngl</scp>o: sculpting protein views on web pages without coding
Bibliographic record
Abstract
MOTIVATION: The sharing of macromolecular structural information online by scientists is predominantly performed via 2D static images, since the embedding of interactive 3D structures in webpages is non-trivial. Whilst the technologies to do so exist, they are often only implementable with significant web coding experience. RESULTS: Michelaɴɢʟo is an accessible and open-source web-based application that supports the generation, customization and sharing of interactive 3D macromolecular visualizations for digital media without requiring programming skills. A PyMOL file, PDB file, PDB identifier code or protein/gene name can be provided to form the basis of visualizations using the NGL JavaScript library. Hyperlinks that control the view can be added to text within the page. Protein-coding variants can be highlighted to support interpretation of their potential functional consequences. The resulting visualizations and text can be customized and shared, as well as embedded within existing websites by following instructions and using a self-contained download. Michelaɴɢʟo allows researchers to move away from static images and instead engage, describe and explain their protein to a wider audience in a more interactive fashion. AVAILABILITY AND IMPLEMENTATION: Michelaɴɢʟo is hosted at michelanglo.sgc.ox.ac.uk. The Python code is freely available at https://github.com/thesgc/MichelaNGLo, along with documentations about its implementation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.284 | 0.255 |
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".