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dc.contributor.authorKueffner, R.
dc.contributor.authorZach, N.
dc.contributor.authorBronfeld, M.
dc.date.accessioned2019-09-13T16:42:05Z
dc.date.available2019-09-13T16:42:05Z
dc.date.issued2019
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85060520844&doi=10.1038%2fs41598-018-36873-4&partnerID=40&md5=cee2822d9e7d404d4b5ec2728f2e6fcd
dc.identifier.urihttp://hdl.handle.net/10713/10764
dc.description.abstractAmyotrophic 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. Copyright The Author(s) 2019.en_US
dc.description.urihttps://doi.org/10.1038/s41598-018-36873-4en_US
dc.language.isoen-USen_US
dc.publisherNature Publishing Groupen_US
dc.relation.ispartofScientific Reports
dc.subjectDREAM Prize4Life ALS Stratification Challengeen_US
dc.subjectstratification of patientsen_US
dc.subject.meshAmyotrophic Laterial Sclerosisen_US
dc.subject.meshCrowdsourcing--methodsen_US
dc.titleStratification of amyotrophic lateral sclerosis patients: A crowdsourcing approachen_US
dc.typeArticleen_US
dc.identifier.doi10.1038/s41598-018-36873-4
dc.identifier.pmid30679616


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