Reference : Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis...
Scientific journals : Article
Life sciences : Biotechnology
http://hdl.handle.net/10993/26440
Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression
English
Küffner, Robert []
Zach, Neta []
Norel, Raquel []
Hawe, Johann []
Schoenfeld, David []
Wang, Liuxia []
Li, Guang []
Fang, Lilly []
Mackey, Lester []
Hardiman, Orla []
Cudkowicz, Merit []
Sherman, Alexander []
Ertaylan, Gökhan mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Grosse-Wentrup, Moritz []
Hothorn, Torsten []
Ligtenberg, Jules van []
Macke, Jakob H []
Meyer, Timm []
Schölkopf, Bernhard []
Tran, Linh []
Vaughan, Rubio []
Stolovitzky, Gustavo []
Leitner, Melanie L []
2015
Nature Biotechnology
Nature Publishing Group
33
1
51-57
Yes (verified by ORBilu)
1087-0156
1546-1696
New York
NY
[en] Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development.
Luxembourg Centre for Systems Biomedicine (LCSB): Computational Biology (Del Sol Group)
http://hdl.handle.net/10993/26440
10.1038/nbt.3051

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