Abstract 2885: The NCI-Nature Pathway Interaction Database: A comprehensive resource for cell signaling information
Bibliographic record
Abstract
Abstract The NCI-Nature Pathway Interaction Database (PID, http://pid.nci.nih.gov) is a freely available collection of professionally curated and expert-reviewed signaling and regulatory pathways composed of human molecular interactions and cellular processes extracted from the primary literature. As of November 2010, the database contains 116 pathways and networks encompassing 7810 interactions, 3531 proteins, 137 small molecules, 3101 complexes, 5930 peer-reviewed publications and more than 10500 uses of evidence codes, and includes recent additions to the Notch and Wnt pathways along with the AP1 network. Created in a collaboration between the U.S. National Cancer Institute and Nature Publishing Group, the PID offers a range of tools to facilitate pathway exploration. Users can browse pathways and create network maps based on molecule or biological process queries; interactive network maps are displayed in JPG, SVG and Silverlight formats. The Batch query tool allows users to overlay molecule lists, such as those derived from microarray data, onto pathways. Users can also download a list of references used to create the pathway, pathway molecule lists, and complete database content in extensible markup language (XML) or Biological Pathways Exchange (BioPAX) Level 2 or Level 3 formats. The database is updated every month and supplemented by a concise editorial section that provides synopses of recent noteworthy papers in cell signaling and specially commissioned articles on the practical uses of other relevant Biomedical Informatics tools. Users can sign up for email alerts or RSS feeds to receive database content updates. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2885. doi:10.1158/1538-7445.AM2011-2885
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.138 | 0.104 |
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".