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dc.contributor.authorKrishnagopal, S.en_US
dc.contributor.authorvon Coelln, R.en_US
dc.contributor.authorShulman, L.M.en_US
dc.date.accessioned2020-06-30T18:46:29Z
dc.date.available2020-06-30T18:46:29Z
dc.date.issued2020
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85086712226&doi=10.1371%2fjournal.pone.0233296&partnerID=40&md5=d831142ab6c5a09db9f3a52d04b769a5
dc.identifier.urihttp://hdl.handle.net/10713/13198
dc.description.abstractChronic medical conditions show substantial heterogeneity in their clinical features and progression. We develop the novel data-driven, network-based Trajectory Profile Clustering (TPC) algorithm for 1) identification of disease subtypes and 2) early prediction of subtype/disease progression patterns. TPC is an easily generalizable method that identifies subtypes by clustering patients with similar disease trajectory profiles, based not only on Parkinson's Disease (PD) variable severity, but also on their complex patterns of evolution. TPC is derived from bipartite networks that connect patients to disease variables. Applying our TPC algorithm to a PD clinical dataset, we identify 3 distinct subtypes/patient clusters, each with a characteristic progression profile. We show that TPC predicts the patient's disease subtype 4 years in advance with 72% accuracy for a longitudinal test cohort. Furthermore, we demonstrate that other types of data such as genetic data can be integrated seamlessly in the TPC algorithm. In summary, using PD as an example, we present an effective method for subtype identification in multidimensional longitudinal datasets, and early prediction of subtypes in individual patients.en_US
dc.description.urihttps://doi.org/10.1371/journal.pone.0233296en_US
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofPloS one
dc.subjectTrajectory Profile Clustering algorithmen_US
dc.subject.meshDisease Progressionen_US
dc.subject.meshForecastingen_US
dc.subject.meshParkinson Diseaseen_US
dc.titleIdentifying and predicting Parkinson's disease subtypes through trajectory clustering via bipartite networksen_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pone.0233296
dc.identifier.pmid32555729


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