Highlights of the Biology and Disease-driven Human Proteome Project, 2015–2016
Bibliographic record
Abstract
The Biology and Disease-driven Human Proteome Project (B/D-HPP) is aimed at supporting and enhancing the broad use of state-of-the-art proteomic methods to characterize and quantify proteins for in-depth understanding of the molecular mechanisms of biological processes and human disease. Based on a foundation of the pre-existing HUPO initiatives begun in 2002, the B/D-HPP is designed to provide standardized methods and resources for mass spectrometry and specific protein affinity reagents and facilitate accessibility of these resources to the broader life sciences research and clinical communities. Currently there are 22 B/D-HPP initiatives and 3 closely related HPP resource pillars. The B/D-HPP groups are working to define sets of protein targets that are highly relevant to each particular field to deliver relevant assays for the measurement of these selected targets and to disseminate and make publicly accessible the information and tools generated. Major developments are the 2016 publications of the Human SRM Atlas and of "popular protein sets" for six organ systems. Here we present the current activities and plans of the BD-HPP initiatives as highlighted in numerous B/D-HPP workshops at the 14th annual HUPO 2015 World Congress of Proteomics in Vancouver, Canada.
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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 | 0.021 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".