Author:
Kueffner Robert,Zach Neta,Bronfeld Maya,Norel Raquel,Atassi Nazem,Balagurusamy Venkat,di Camillo Barbara,Chio Adriano,Cudkowicz Merit,Dillenberger Donna,Garcia-Garcia Javier,Hardiman Orla,Hoff Bruce,Knight Joshua,Leitner Melanie L.,Li Guang,Mangravite Lara,Norman Thea,Wang Liuxia,Xiao Jinfeng,Fang Wen-Chieh,Peng Jian,Stolovitzky Gustavo,
Abstract
AbstractAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in clinical presentation with an urgent need for better stratification tools for clinical development and care. In this study we used a crowdsourcing approach to address the problem of ALS patient stratification. The DREAM Prize4Life ALS Stratification Challenge was a crowdsourcing initiative using data from >10,000 patients from completed ALS clinical trials and 1479 patients from community-based patient registers. Challenge participants used machine learning and clustering techniques to predict ALS progression and survival. By developing new approaches, the best performing teams were able to predict disease outcomes better than currently available methods. At the same time, the integration of clustering components across methods led to the emergence of distinct consensus clusters, separating patients into four consistent groups, each with its unique predictors for classification. This analysis reveals for the first time the potential of a crowdsourcing approach to uncover covert patient sub-populations, and to accelerate disease understanding and therapeutic development.
Publisher
Cold Spring Harbor Laboratory
Cited by
1 articles.
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