MassVis: Visual analysis of protein complexes using mass spectrometry
Bibliographic record
Abstract
Protein complexes are formed when two or more proteins non-covalently interact to form a larger three dimensional structure with specific biological function. Understanding the composition of such complexes is vital to understanding cell biology at the molecular level. MassVis is a visual analysis tool designed to assist the interpretation of data from a new workflow for detecting the composition of such protein complexes in biological samples. The data generated by the laboratory workflow naturally lends itself to a scatter plot visualization. However, characteristics of this data give rise to some unique aspects not typical of a standard scatter plot. We are able to take the output from tandem mass spectrometry and render the data in such a way that it mimics more traditional two-dimensional gel techniques and at the same time reveals the correlated behavior indicative of protein complexes. By computationally measuring these correlated patterns in the data, membership in putative complexes can be inferred. User interactions are provided to support both an interactive discovery mode as well as an unsupervised clustering of likely complexes. The specific analysis tasks led us to design a unique arrangement of item selection and coordinated detail views in order to simultaneously view different aspects of the selected item.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".