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Kueffner, R.* ; Zach, N.* ; Bronfeld, M.* ; Norel, R.* ; Atassi, N.* ; Balagurusamy, V.* ; di Camillo, B.* ; Chio, A.* ; Cudkowicz, M.* ; Dillenberger, D.* ; Garcia-Garcia, J.* ; Hardiman, O.* ; Hoff, B.* ; Knight, J.* ; Leitner, M.L.* ; Li, G.* ; Mangravite, L.* ; Norman, T.* ; Wang, L.* ; Alkallas, R.* ; Anghel, C.* ; Avril, J.* ; Bacardit, J.* ; Balser, B.* ; Balser, J.* ; Bar-Sinai, Y.* ; Ben-David, N.* ; Ben-Zion, E.* ; Bliss, R.* ; Cai, J.* ; Chernyshev, A.* ; Chiang, J.* ; Chicco, D.* ; Corriveau, B.A.N.* ; Dai, J.* ; Deshpande, Y.* ; Desplats, E.* ; Durgin, J.S.* ; Espiritu, S.M.G.* ; Fan, F.* ; Fevrier, P.* ; Fridley, B.L.* ; Godzik, A.* ; Golinska, A.* ; Gordon, J.* ; Graw, S.* ; Guo, Y.* ; Herpelinck, T.* ; Hopkins, J.* ; Huang, B.* ; Jacobsen, J.* ; Jahandideh, S.* ; Jeon, J.* ; Ji, W.* ; Jung, K.* ; Karanevich, A.* ; Koestler, D.C.* ; Kozak, M.* ; Kurz, C.F. ; Lalansingh, C.* ; Larrieu, T.* ; Lazzarini, N.* ; Lerner, B.* ; Lesinski, W.* ; Liang, X.* ; Lin, X.* ; Lowe, J.* ; Mackey, L.* ; Meier, R.* ; Min, W.* ; Mnich, K.* ; Nahmias, V.* ; Noel-MacDonnell, J.* ; O'Donnell, A.* ; Paadre, S.* ; Park, J.* ; Polewko-Klim, A.* ; Raghavan, R.* ; Rudnicki, W.* ; Saghapour, E.* ; Salomond, J.* ; Sankaran, K.* ; Sendorek, D.* ; Sharan, V.* ; Shiah, Y.* ; Sirois, J.* ; Sumanaweera, D.N.* ; Usset, J.* ; Vang, Y.S.* ; Vens, C.* ; Wadden, D.* ; Wang, D.* ; Wong, W.C.* ; Xie, X.* ; Xu, Z.* ; Yang, H.* ; Yu, X.* ; Zhang, H.* ; Zhang, L.* ; Zhang, S.* ; Zhu, S.* ; Xiao, J.* ; Fang, W.* ; Peng, J.* ; Yang, C.* ; Chang, H.* ; Stolovitzky, G.*

Stratification of amyotrophic lateral sclerosis patients: A crowdsourcing approach.

Sci. Rep. 9:690 (2019)
Publ. Version/Full Text Research data DOI
Open Access Gold
Creative Commons Lizenzvertrag
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from > 10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Body-mass Index; Disease Progression; Genetic-heterogeneity; Outcome Measures; Double-blind; Creatinine; Survival; Trial; Predictors; Dexpramipexole
ISSN (print) / ISBN 2045-2322
e-ISSN 2045-2322
Journal Scientific Reports
Quellenangaben Volume: 9, Issue: , Pages: , Article Number: 690 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Health Economics and Health Care Management (IGM)