Advances in Identifying and Characterizing the Human Proteome
Bibliographic record
Abstract
On the basis of the January 2019 updates on the curation and annotation of the human proteome by neXtProt4 (https://www.nextprot.org/) from the standardized reanalysis of mass spectrometry (MS) data sets by PeptideAtlas5 (http://www.\npeptideatlas.org/), which form the baseline for the 2019 papers in this special issue, Omenn et al. (10.1021/acs.jproteome.9b00434) report that highly credible findings supportProtein Evidence Level 1 (PE1) for 17 694 protein-coding genes, 89.3% of the total 19 823 predicted human proteincoding genes. In this annual metric of the human proteome paper, these numbers represent a net increase of 224 PE1 proteins due to the promotion of 213 PE2,3,4 entries to PE1 and\na net decrease of only 57 missing proteins (MPs) due to 60 PE1 demotions and 116 new PE2,3,4 entries, primarily immunoglobulins. The dynamics of the year-to-year changes in neXtProt are quite complex, as explained in this article. A chromosome-bychromosome analysis showed that four chromosomes (1, 5, 17, 19) have had reductions of between 50 and 130 MPs, and another four (2, 3, 10, X) have had 43−47 fewer MPs since the baseline for the neXt-MP50 Challenge in 2016. Utilizing ProteomeXchange, PRIDE, and other data resources, PeptideAtlas documented an increase of 495 canonical proteins from 120 new human sample data sets; three data sets from the JPR 2018 special issue contributed a major proportion of these with 161 of the 495. The canonical proteins from PeptideAtlas account for 98% of the 16 600 PE1 neXtProt entries based on MS, compliant with the stringent HPP Guidelines v2.1.6 More than half of the new canonical proteins were already PE1 but were based on other kinds of non-mass-spectrometry protein evidence
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".