Novel biomarkers and protein-protein interaction in multiple myeloma
Bibliographic record
Abstract
The Biomarkers of disease’ project was designed to use proteomics and bioinformatics, in order to discover novel biomarkers associated with different diseases, that can be used as a diagnostic tool or to aid in target drug therapy. In this paper, we focus on multiple myeloma (MM), which is the second most common hematological malignancy with an incidence of 55 per 1 000 000 people in Canada . Multiple myeloma is a malignancy of plasma cells, which are differentiated B lymphocytes, and they function to produce antibodies in response to pathogens That said, plasma cells also play a role in the development of allergies and autoimmune diseases. Plasma cells that are short lived are easily depleted by drugs that slows or stops the growth of cells, however, long-lived plasma cells may give rise to autoantibodies that are relatively resistant to therapy. Multiple myeloma arises when plasma cells are poorly regulated. The cause of multiple myeloma is poorly understood, but a study in Canada has shown that the occurrence of MM is more common in rural areas and agricultural cities (5). This gives rise to the identification of pesticides and chemicals used for farming as risk factors in the pathogenesis of MM. Well known risk factors for MM are advancing age, black race, and male sex (6), as well as other factors including obesity, ionising radiation, and infections with HIV and Hepatitis C. The typical symptoms of MM can be summarized into the acronym ‘CRAB’, which stands for hypercalcemia, renal failure, anemia, and bone disease . Although not all patients exhibit the usual CRAB symptoms; a study demonstrated that only 74% of MM patients displayed the typical symptoms of CRAB while 20% had different symptoms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".