Title |
Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression
|
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Published in |
Nature Biotechnology, November 2014
|
DOI | 10.1038/nbt.3051 |
Pubmed ID | |
Authors |
Robert Küffner, Neta Zach, Raquel Norel, Johann Hawe, David Schoenfeld, Liuxia Wang, Guang Li, Lilly Fang, Lester Mackey, Orla Hardiman, Merit Cudkowicz, Alexander Sherman, Gokhan Ertaylan, Moritz Grosse-Wentrup, Torsten Hothorn, Jules van Ligtenberg, Jakob H Macke, Timm Meyer, Bernhard Schölkopf, Linh Tran, Rubio Vaughan, Gustavo Stolovitzky, Melanie L Leitner |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 36 | 28% |
United Kingdom | 14 | 11% |
France | 5 | 4% |
Canada | 2 | 2% |
Netherlands | 2 | 2% |
Spain | 2 | 2% |
Switzerland | 2 | 2% |
Indonesia | 1 | <1% |
Australia | 1 | <1% |
Other | 8 | 6% |
Unknown | 57 | 44% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 94 | 72% |
Scientists | 23 | 18% |
Practitioners (doctors, other healthcare professionals) | 8 | 6% |
Science communicators (journalists, bloggers, editors) | 5 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 2% |
Germany | 1 | <1% |
Netherlands | 1 | <1% |
United Kingdom | 1 | <1% |
Korea, Republic of | 1 | <1% |
Spain | 1 | <1% |
Ukraine | 1 | <1% |
Unknown | 241 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 59 | 24% |
Student > Ph. D. Student | 49 | 20% |
Student > Master | 34 | 14% |
Other | 19 | 8% |
Student > Bachelor | 14 | 6% |
Other | 53 | 21% |
Unknown | 23 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 59 | 24% |
Medicine and Dentistry | 32 | 13% |
Biochemistry, Genetics and Molecular Biology | 28 | 11% |
Computer Science | 25 | 10% |
Neuroscience | 17 | 7% |
Other | 49 | 20% |
Unknown | 41 | 16% |