Abstract LB-130: The NCI-Nature Pathway Interaction Database: A cell signaling resource
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, signaling events and cellular processes extracted from primary literature. Pathways selected for curation are based on potential drug targets, suggestions made by our users and reviewers, and other prominent cell signaling molecules. As of February 2010, the database contains 106 pathways encompassing 6696 interactions, 3231 proteins, 142 small molecules, 2622 complexes and 4843 peer-reviewed publications, and includes recent additions to both the p53 and EGFR pathways. Created in a collaboration between the U.S. National Cancer Institute and Nature Publishing Group, the PID is aimed at researchers interested in cell signaling pathways, such as molecular cell biologists, and bioinformaticians. The database offers a range of tools to facilitate pathway exploration. Users can browse the pre-defined set of pathways and create network maps centered on a single molecule or biological process of interest. The Batch query tool allows users to upload molecule lists, such as those derived from microarray data, and visualize the resulting molecular connectivity map. In addition, users can download lists of proteins, references used to create the pathway and complete database content in extensible markup language (XML) or Biological Pathways Exchange (BioPAX) format. 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 Bioinformatics tools. Users can sign up for email alerts or RSS feeds to receive database updates. 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 LB-130.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.109 | 0.088 |
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".