Abstract 92: dbCPCO: The database of genetic predictive and prognostic factors in colorectal cancer
Bibliographic record
Abstract
Abstract Introduction: Colorectal cancer is the third most common cancer in the North America with a 5-year survival rate of ∼65%. A portion of the inter-patient variability in disease outcome in colorectal cancer patients is attributed to inherited and somatic genetic factors. Identification of these genetic factors helps implementing personalized medicare to improve the chances of cure in patients. While numerous research articles have investigated the genetic basis of clinical outcome in colorectal cancer, there has not been a central resource, such as a public database, that compiles these findings in a systematic, comprehensive and concise way. Here we describe the dbCPCO, a database of genetic factors investigated in relation to clinicopathological characteristics and clinical outcome (i.e. treatment response and toxicity, prognosis and survival) in colorectal cancer. Methods: Literature reports were retrieved from PUBMED. The data that met the inclusion criteria were compiled in a relational database and posted in a dedicated website. Results: The genetic factors included the inherited genetic polymorphisms, somatic and germline mutations in both nuclear and mitochondrial DNA. As of November 2009, the dbCPCO website posts 610 scientific findings on >300 polymorphisms, somatic and germline mutations from 146 genes tested for correlation with clinicopathological and/or clinical outcome in colorectal cancer. Conclusion: The dbCPCO is a unique and comprehensive database that compiles literature findings on the genetic basis of disease phenotype and clinical outcome in colorectal cancer and will be instrumental in expediting personalized medicine and further research in this disease. The dbCPCO is updated periodically and is freely available for the scientific and medical community at http://www.med.mun.ca/cpco/Default.aspx. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 92.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.028 | 0.045 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".