Abstract 51: International Cancer Genome Consortium Data Portal – A ‘one-stop-shop’ for genomic, transcriptomic, and epigenomic data
Bibliographic record
Abstract
Abstract The International Cancer Genome Consortium (ICGC) (www.icgc.org) orchestrates a multi-national effort to catalogue genomic abnormalities in 50 different tumor types. For each type 500 pairs of matched tumor and normal tissues will be studied using multiple technologies including DNA sequencing, expression profiling, and epigenetic analysis. ICGC data will be linked to cancer etiology, drug response, and patient survival. Each ICGC member institute generates its site-specific data independently and stores it in a local database. These databases are federated using BioMart technology (www.biomart.org) and made available to the public through the ICGC data portal (dcc.icgc.org). To the user, the multiple databases appear as a single integrated database. The ICGC data portal provides a platform for scientists to search, download, and analyze a broad range of cancer data. In addition to the data generated by the ICGC members, the portal includes data from other projects such as The Cancer Genome Atlas (TCGA) and Tumor Sequencing Project (TSP). Currently, the portal contains data sets from 15 cancer types. Data sets are divided into two categories: open access and controlled access. Open access data sets are available to the entire research community and include somatic mutations (simple, copy number variations, and structural rearrangements), gene and miRNA expression, DNA methylation, and exon junctions. Controlled access data sets, such as germline mutations, are available to users who have been authorized according to ICGC policies. The portal also includes annotation data from other publically available databases such as Ensembl, KEGG, Reactome, and the Pancreatic Expression Database. All data in the portal is exposed through a variety of user interfaces and can be processed using available analytical and visualization tools. For instance, users can perform gene- or pathway-centric analyses, such as compare mutation frequencies in specific genes or pathways, or compare mutation patterns between multiple tumor samples. Furthermore, by selecting diverse query and output criteria, users can retrieve data based on different molecular and clinical covariates. The portal enables users to conveniently retrieve, characterize, and compare cancer data from different sources through a single point of access. As more ICGC data is added and portal's functionality is further enhanced, it is expected that it will become an increasingly important resource for cancer researchers with diverse user requirements. 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 51. doi:10.1158/1538-7445.AM2011-51
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.240 | 0.176 |
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".