Abstract 923: The cBioPortal for Cancer Genomics: An intuitive open-source platform for exploration, analysis and visualization of cancer genomics data
Bibliographic record
Abstract
Abstract The cBioPortal for Cancer Genomics is an open source software platform that enables interactive, exploratory analysis of large-scale cancer genomics data sets. It integrates genomic and clinical data, and provides a suite of visualization and analysis options, including cohort and patient-level visualization, mutation visualization, survival analysis, alteration enrichment analysis, and network analysis. The user interface is user-friendly, responsive, and makes genomic data easily accessible to scientists and clinicians. The public site (http://www.cbioportal.org) hosts data from more than 165 studies, including data from large consortia (TCGA and ICGC) and individual labs. With newly released functionality, users can now explore and query these studies individually or can combine multiple studies into new “virtual studies”. The main features of the portal include OncoPrints, a compact graphical representation of alterations in multiple genes across a cohort, mutational diagrams that show locations and frequencies of mutations in a single gene, Kaplan-Meier survival curves, plots that allow the visualization of correlation between different data types for a single or multiple genes (e.g. the correlation between DNA copy number and mRNA expression), among others. To facilitate interpretation of genomic data, the cBioPortal also now integrates annotations from several leading knowledgebases (OncoKB, CIViC, MyCancerGenome and COSMIC), as well as other resources that can guide variant interpretation (CancerHotspots, MutationAssessor, SIFT and PolyPhen). The cBioPortal has been widely adopted by the cancer community, with dozens of private instances at academic institutions and pharmaceutical/biotechnology companies. The public portal is currently accessed by approximately 25K unique visitors per month. Another notable instance is the cBioPortal for AACR GENIE (http://www.cbioportal.org/genie/), which hosts 31,706 samples from AACR Project GENIE. The cBioPortal is fully open source and all code is available on GitHub (https://github.com/cBioPortal/) under a GNU Affero GPL license. Development is a collaborative effort among groups at Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, Children's Hospital of Philadelphia, Princess Margaret Cancer Centre, and The Hyve, an open source bioinformatics company based in the Netherlands. Ongoing development efforts are focused on (1) building the open source community; (2) implementing architectural and performance improvements; (3) expanding user support, documentation and training resources; (4) developing novel features to support immunogenomics and immunotherapy; (5) enhancing visualization of patient timelines, multiple tumor profiles, and cohort response; and (6) releasing a new public Application Programming Interface (API). Citation Format: Jianjiong Gao, Tali Mazor, Ersin Ciftci, Pichai Raman, Pieter Lukasse, Istemi Bahceci, Alexandros Sigaras, Adam Abeshouse, Ino de Bruijn, Benjamin Gross, Ritika Kundra, Aaron Lisman, Angelica Ochoa, Robert Sheridan, Jing Su, Selcuk O. Sumer, Yichao Sun, Avery Wang, Jiaojiao Wang, Manda Wilson, Hongxin Zhang, Priti Kumari, James Lindsay, Karthik Kalletla, Kelsey Zhu, Oleguer Plantalech, Fedde Schaeffer, Sander Tan, Dionne Zaal, Sjoerd van Hagen, Kees van Bochove, Ugur Dogrusoz, Trevor J. Pugh, Adam Resnick, Chris Sander, Nikolaus Schultz, Ethan Cerami. The cBioPortal for Cancer Genomics: An intuitive open-source platform for exploration, analysis and visualization of cancer genomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 923.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.044 |
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".