Abstract 4278: International Cancer Genome Consortium (ICGC)
Bibliographic record
Abstract
Abstract The International Cancer Genome Consortium (ICGC) was established to bring together researchers from around the globe to comprehensively analyze the genomic, transcriptomic, and epigenomic changes in 50 different tumor types or subtypes that are of clinical and societal importance across the globe (International network of cancer genome projects. Nature 464, 993-998 (15 April 2010)). As of December 2013, the ICGC has received commitments from researchers and funding organizations in Asia, Australia, Europe, North America, and South America for 69 project teams in 17 jurisdictions to study more than 25,000 tumor genomes. Over 27,552 tumors that meet ICGC requirements for consent have been obtained, and research pathologists have approved over 24,160 samples with project-confirmed diagnosis as meeting ICGC standards. Processed data is available via the Data Coordination Centre (http://dcc.icgc.org) based at the Ontario Institute for Cancer Research and is updated bimonthly. The latest data release (Version 14) in September 2013 includes datasets from 8532 donors participating in 41 ICGC project teams. The ICGC Data Portal has been improved to provide enhanced support to the research community browsing this vast dataset. ICGC researchers plan to undertake a suite of studies called the Pan-Cancer Analysis Project (PCAP). The analyses will begin in early 2014 using whole genome sequencing (WGS) datasets from approximately 2,000 matched tumour-normal specimen pairs representing more than a dozen different tumour types. It is expected that this effort will lead to the identification of driver mutations outside coding regions including those that affect cis-regulatory regions and non-coding RNAs, and identification of gene regulatory pathways altered by non-coding mutations. Because the projected size of the pan-cancer dataset, i.e. 4,000 WGS, is very large, PCAP will need to use a compute cloud environment (involving multiple computing centers in the USA, Europe, and Asia) that will meet technical requirements of the project and the bioethical framework of ICGC and its member projects. More information can be found on www.icgc.org. Citation Format: Thomas J. Hudson, Jennifer L. Jennings. International Cancer Genome Consortium (ICGC). [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4278. doi:10.1158/1538-7445.AM2014-4278
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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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.030 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.083 | 0.039 |
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".