The Lucid Proteomics System for top-down biomarker research
Bibliographic record
Abstract
Advances have been made in recent years for both "top-down" and "bottom-up" profiling approaches to biomarker discovery. Top-down protein profiling via SELDI-TOF mass spectrometry has been used by researchers in many fields of study to discover native protein biomarker candidates from a variety of sample types, but has been limited without a means for straightforward identification of these candidates. Bio-Rad has recently partnered with Bruker Daltonics to create the Lucid Proteomics System, a complete SELDI-based research workflow--system qualification, biomarker discovery, data analysis, and biomarker purification/identification--using Bruker's flex series of TOF and TOF/TOF mass spectrometers, which have long provided consistent performance and high value data for MALDI applications. This collaboration enables both top-down and bottom-up proteomics approaches on a single high performance MALDI-TOF MS platform for maximum coverage of the proteome--allowing greater flexibility with experimental design and accelerating biomarker research programmes.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.017 |
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".