Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

Robert Küffner*, Neta Zach, Raquel Norel, Johann Hawe, David Schoenfeld, Liuxia Wang, Guang Li, Lilly Fang, Lester Mackey, Orla Hardiman, Merit Cudkowicz, Alexander Sherman, Gökhan Ertaylan, Moritz Grosse-Wentrup, Torsten Hothorn, Jules van Ligtenberg, Jakob H Macke, Timm Meyer, Bernhard Schölkopf, Linh TranRubio Vaughan, Gustavo Stolovitzky, Melanie L Leitner

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)51-7
Number of pages7
JournalNature Biotechnology
Issue number1
Publication statusPublished - Jan 2015


  • Algorithms
  • Amyotrophic Lateral Sclerosis
  • Clinical Trials as Topic
  • Crowdsourcing
  • Disease Progression
  • Humans

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