Bibliographic record
Abstract
BACKGROUND: Most of newly discovered cancer biomarkers fail in the clinic because they lack sensitivity and/or specificity. The current explosion in knowledge of the mutational spectrum of many cancer types, as a result of whole exome and whole genome sequencing, has revealed a wide spectrum of mutations that appear to be highly specific for various cancer types. DISCUSSION: Mass spectrometry (MS) has the ability to monitor tryptic peptides in complex biological mixtures with high sensitivity and specificity. It may be possible in the near future to combine the known spectrum of gene mutations revealed by genomics with the power of MS, in order to quantify mutant peptides that are highly specific for cancer, in a multiplex fashion. Such mutant peptides, quantified in the circulation and other fluids, may represent tumor markers that are suitable for detection and monitoring of cancer. SUMMARY: The power of genomic and proteomic technologies can be combined to identify highly specific analytes for biomarker applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".