Proteomic technology for biomarker profiling in cancer: an update
Bibliographic record
Abstract
The progress in the understanding of cancer progression and early detection has been slow and frustrating due to the complex multifactorial nature and heterogeneity of the cancer syndrome. To date, no effective treatment is available for advanced cancers, which remain a major cause of morbidity and mortality. Clearly, there is urgent need to unravel novel biomarkers for early detection. Most of the functional information of the cancer-associated genes resides in the proteome. The later is an exceptionally complex biological system involving several proteins that function through posttranslational modifications and dynamic intermolecular collisions with partners. These protein complexes can be regulated by signals emanating from cancer cells, their surrounding tissue microenvironment, and/or from the host. Some proteins are secreted and/or cleaved into the extracellular milieu and may represent valuable serum biomarkers for diagnosis purpose. It is estimated that the cancer proteome may include over 1.5 million proteins as a result of posttranslational processing and modifications. Such complexity clearly highlights the need for ultra-high resolution proteomic technology for robust quantitative protein measurements and data acquisition. This review is to update the current research efforts in high-resolution proteomic technology for discovery and monitoring cancer biomarkers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".