A curated database of genetic markers from the angiogenesis/VEGF pathway and their relation to clinical outcome in human cancers
Bibliographic record
Abstract
INTRODUCTION: Angiogenesis causes local growth, aggressiveness and metastasis in solid tumors, and thus, is almost always associated with poor prognosis and survival in cancer patients. Because of this clinical importance, several chemotherapeutic agents targeting angiogenesis have also been developed. Genes and genetic variations in angiogenesis/VEGF pathway thus may be correlated with clinical outcome in cancer patients. MATERIAL AND METHODS: Here, we describe a manually curated public database, dbANGIO, which posts the results of studies testing the possible correlation of genetic variations (polymorphisms and mutations) from the angiogenesis/VEGF pathway with demographic features, clinicopathological features, treatment response and toxicity, and prognosis and survival-related endpoints in human cancers. The scientific findings are retrieved from PUBMED and posted in the dbANGIO website in a summarized form. RESULTS AND CONCLUSION: As of September 2011, dbANGIO includes 362 entries from 83 research articles encompassing 154 unique genetic variations from 39 genes investigated in several solid and hematological cancers. By curating the literature findings and making them freely available to researchers, dbANGIO will expedite the research on genetic factors from the angiogenesis pathway and will assist in their utility in clinical management of cancer patients. dbANGIO is freely available for non-profit institutions at http://www.med.mun.ca/angio.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".