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DC Field | Value | Language |
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dc.contributor.author | Harvey, J | - |
dc.contributor.author | Reijnders, RA | - |
dc.contributor.author | Cavill, R | - |
dc.contributor.author | Duits, A | - |
dc.contributor.author | Köhler, S | - |
dc.contributor.author | Eijssen, L | - |
dc.contributor.author | Rutten, BPF | - |
dc.contributor.author | Shireby, G | - |
dc.contributor.author | Torkamani, A | - |
dc.contributor.author | Creese, B | - |
dc.contributor.author | Leentjens, AFG | - |
dc.contributor.author | Lunnon, K | - |
dc.contributor.author | Pishva, E | - |
dc.date.accessioned | 2023-11-16T18:13:49Z | - |
dc.date.available | 2023-11-16T18:13:49Z | - |
dc.date.issued | 2022-11-07 | - |
dc.identifier | ORCID iD: Joshua Harvey https://orcid.org/0000-0001-6423-9983 | - |
dc.identifier | ORCID iD: Rick A. Reijnders https://orcid.org/0000-0001-7599-0385 | - |
dc.identifier | ORCID iD: Rachel Cavill https://orcid.org/0000-0002-3796-1687 | - |
dc.identifier | ORCID iD: Annelien Duits https://orcid.org/0000-0003-0279-1806 | - |
dc.identifier | ORCID iD: Lars Eijssen https://orcid.org/0000-0002-6473-2839 | - |
dc.identifier | ORCID iD: Byron Creese https://orcid.org/0000-0001-6490-6037 | - |
dc.identifier | ORCID iD: Ehsan Pishva http://orcid.org/0000-0002-8964-0682 | - |
dc.identifier | 150 | - |
dc.identifier.citation | Harvey, J. (2022) ''Machine learning-based prediction of cognitive outcomes in de novo Parkinson’s disease, npj Parkinson's Disease, 2022, 8 (1), 150, pp. 1 - 11. doi: 10.1038/s41531-022-00409-5 | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/27655 | - |
dc.description | Data availability: Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/access-dataspecimens/download-data). For up-to-date information on the study, visit ppmi-info.org. | en_US |
dc.description | Code availability: All codes are available at https://github.com/Rrtk2/PPMI-ML-Cognition-PD. | - |
dc.description.abstract | Copyright © The Author(s) 2022. Cognitive impairment is a debilitating symptom in Parkinson’s disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson’s Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables. | en_US |
dc.description.sponsorship | This work was supported through a ZonMw Memorabel Grant (733050516) to E.P. J.H. and K.L. are supported by funding from a Medical Research Council Grant (MR/S011625/1). J.H. is supported by the Charles Wolfson Charitable Trust. K.L. is supported by a grant from BRACE Dementia Research. We thank all participants and teams who contributed data to PPMI. PPMI, a public-private partnership, is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners including 4D Pharma, AbbVie, AcureX Therapeutics, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson’s (ASAP), Avid Radiopharmaceuticals, Bial Biotech, Biogen, BioLegend, Bristol Myers Squibb, Calico Life Sciences LLC, Celgene Corporation, DaCapo Brainscience, Denali Therapeutics, The Edmond J. Safra Foundation, Eli Lilly and Company, GE Healthcare, GlaxoSmithKline, Golub Capital, Handl Therapeutics, Insitro, Janssen Pharmaceuticals, Lundbeck, Merck & Co., Meso Scale Diagnostics LLC, Neurocrine Biosciences, Pfizer, Piramal Imaging, Prevail Therapeutics, F. Hoffmann‐La Roche and its affiliated company Genentech, Sanofi Genzyme, Servier, Takeda Pharmaceutical Company, Teva Neuroscience, UCB, Vanqua Bio, Verily Life Sciences, Voyager Therapeutics and Yumanity Therapeutics. Publication was supported by central open access funds from the University of Exeter. | en_US |
dc.format.extent | 1 - 11 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Springer Nature | en_US |
dc.relation.uri | https://github.com/Rrtk2/PPMI-ML-Cognition-PD | - |
dc.relation.uri | https://www.ppmi-info.org/access-dataspecimens/download-data | - |
dc.rights | Copyright © The Author(s) 2022. Rights and permissions: Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | Parkinson's disease | en_US |
dc.subject | predictive markers | en_US |
dc.title | Machine learning-based prediction of cognitive outcomes in de novo Parkinson’s disease | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1038/s41531-022-00409-5 | - |
dc.relation.isPartOf | npj Parkinson's Disease | - |
pubs.issue | 1 | - |
pubs.publication-status | Published | - |
pubs.volume | 8 | - |
dc.identifier.eissn | 2373-8057 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Life Sciences Research Papers |
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