PRISM, a Generic Large Scale Proteomic Investigation Strategy for Mammals*S
Bibliographic record
Abstract
We have developed a systematic analytical approach, termed PRISM (Proteomic Investigation Strategy for Mammals), that permits routine, large scale protein expression profiling of mammalian cells and tissues. PRISM combines subcellular fractionation, multidimensional liquid chromatography-tandem mass spectrometry-based protein shotgun sequencing, and two newly developed computer algorithms, STATQUEST and GOClust, as a means to rapidly identify, annotate, and categorize thousands of expressed mammalian proteins. The application of PRISM to adult mouse lung and liver resulted in the high confidence identification of over 2,100 unique proteins including more than 100 integral membrane proteins, 400 nuclear proteins, and 500 uncharacterized proteins, the largest proteome study carried out to date on this important model organism. Automated clustering of the identified proteins into Gene Ontology annotation groups allowed for streamlined analysis of the large data set, revealing interesting and physiologically relevant patterns of tissue and organelle specificity. PRISM therefore offers an effective platform for in-depth investigation of complex mammalian proteomes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".