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Küffner, R. ; Zach, N.* ; Norel, R.* ; Hawe, J.* ; Schoenfeld, D.* ; Wang, L.* ; Li, G.* ; Fang, L.* ; Mackey, L.* ; Hardiman, O.* ; Cudkowicz, M.* ; Sherman, A.* ; Ertaylan, G.* ; Grosse-Wentrup, M.* ; Hothorn, T.* ; van Ligtenberg, J.* ; Macke, J.H.* ; Meyer, T.* ; Schölkopf, B.* ; Tran, L.* ; Vaughan, R.* ; Stolovitzky, G.* ; Leitner, M.L.*

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

Nat. Biotechnol. 33, 51-57 (2015)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Uric-acid Levels; Body-mass Index; Functional Rating-scale; Alsfrs-r Score; Disease Progression; Prognostic-factors; Parkinson-disease; Blood-pressure; Survival; Population
ISSN (print) / ISBN 1087-0156
e-ISSN 1546-1696
Zeitschrift Nature Biotechnology
Quellenangaben Band: 33, Heft: 1, Seiten: 51-57 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)