The Prostate Cancer DREAM Challenge: A Community-Wide Effort to Use Open Clinical Trial Data for the Quantitative Prediction of Outcomes in Metastatic Prostate Cancer-Reference-Cited by-同舟云学术

The Prostate Cancer DREAM Challenge: A Community-Wide Effort to Use Open Clinical Trial Data for the Quantitative Prediction of Outcomes in Metastatic Prostate Cancer

Published:2015-03-16 Issue:5 Volume:20 Page:459-460
ISSN:1083-7159
Container-title:The Oncologist
language:en
Short-container-title:

Author:

Abdallah Kald¹,Hugh-Jones Charles²,Norman Thea³,Friend Stephen³,Stolovitzky Gustavo⁴⁵

Affiliation:

1. Project Data Sphere, Raleigh, North Carolina, USA;

2. Sanofi US, Bridgewater, New Jersey, USA;

3. Sage Bionetworks, Seattle, Washington, USA;

4. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA;

5. Icahn School of Medicine at Mount Sinai, New York, New York, USA

Abstract

Project Data Sphere and Sage Bionetworks/DREAM are launching the Prostate Cancer DREAM Challenge to improve a predictive model of disease progression and treatment toxicity in prostate cancer using historical trial data. Predictions identified through this challenge have the potential to translate into reduced trial redundancy, better clinical decision tools, and improved patient outcomes. The challenge launches on March 16, 2015.

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Oncology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1634/theoncologist.2015-0054

Reference6 articles.

1. Toward better benchmarking: Challenge-based methods assessment in cancer genomics;Boutros;Genome Biol,2014

2. Analysis of CASP8 targets, predictions and assessment methods;Shi,2009

3. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge;Brownstein;Genome Biol,2014

4. DREAMing of benchmarks;Jarchum;Nat Biotechnol,2015

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