Open Data to Support CANCER Science—A Bioinformatics Perspective on Glioma Research
Bibliographic record
Abstract
Supporting data sharing is paramount to making progress in cancer research. This includes the search for more precise targeted therapies and the search for novel biomarkers, through cluster and classification analysis, and extends to learning details in signal transduction pathways or intra- and intercellular interactions in cancer, through network analysis and network simulation. Our work aims to support and promote the use of publicly available resources in cancer research and demonstrates artificial intelligence (AI) methods to find answers to detailed questions. For example, how targeted therapies can be developed based on precision medicine or how to investigate cell-level phenomena with the help of bioinformatical methods. In our paper, we illustrate the current state of the art with examples from glioma research, in particular, how open data can be used for cancer research in general, and point out several resources and tools that are readily available. Presently, cancer researchers are often not aware of these important resources.
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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 | 0.033 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".