Amit Khanna, Jamie Adams, Chrystalina Antoniades, Bastiaan R. Bloem, Camille Carroll, Jesse Cedarbaum, Joshua Cosman, David T. Dexter, Marissa F. Dockendorf, Jeremy Edgerton, Laura Gaetano, Erkuden Goikoetxea, Derek Hill, Fay Horak, Elena S. Izmailova, Tairmae Kangarloo, Dina Katabi, Catherine Kopil, Michael Lindemann, Jennifer Mammen, Kenneth Marek, Kevin McFarthing, Anat Mirelman, Martijn Muller, Gennaro Pagano, M. Judith Peterschmitt, Jie Ren, Lynn Rochester, Sakshi Sardar, Andrew Siderowf, Tanya Simuni, Diane Stephenson, Christine Swanson-Fischer, John A. Wagner, Graham B. Jones
{"title":"Accelerating Parkinson’s Disease drug development with federated learning approaches","authors":"Amit Khanna, Jamie Adams, Chrystalina Antoniades, Bastiaan R. Bloem, Camille Carroll, Jesse Cedarbaum, Joshua Cosman, David T. Dexter, Marissa F. Dockendorf, Jeremy Edgerton, Laura Gaetano, Erkuden Goikoetxea, Derek Hill, Fay Horak, Elena S. Izmailova, Tairmae Kangarloo, Dina Katabi, Catherine Kopil, Michael Lindemann, Jennifer Mammen, Kenneth Marek, Kevin McFarthing, Anat Mirelman, Martijn Muller, Gennaro Pagano, M. Judith Peterschmitt, Jie Ren, Lynn Rochester, Sakshi Sardar, Andrew Siderowf, Tanya Simuni, Diane Stephenson, Christine Swanson-Fischer, John A. Wagner, Graham B. Jones","doi":"10.1038/s41531-024-00837-5","DOIUrl":null,"url":null,"abstract":"<p>Parkinson’s Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to design, capture and interrogate substantial clinical datasets. Herein we advocate how advances in the deployment of artificial intelligence models for Federated Data Analysis and Federated Learning can help spearhead coordinated and sustainable approaches to address this grand challenge.</p>","PeriodicalId":19706,"journal":{"name":"NPJ Parkinson's Disease","volume":"73 1","pages":""},"PeriodicalIF":6.7000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Parkinson's Disease","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41531-024-00837-5","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Parkinson’s Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to design, capture and interrogate substantial clinical datasets. Herein we advocate how advances in the deployment of artificial intelligence models for Federated Data Analysis and Federated Learning can help spearhead coordinated and sustainable approaches to address this grand challenge.
期刊介绍：
npj Parkinson's Disease is a comprehensive open access journal that covers a wide range of research areas related to Parkinson's disease. It publishes original studies in basic science, translational research, and clinical investigations. The journal is dedicated to advancing our understanding of Parkinson's disease by exploring various aspects such as anatomy, etiology, genetics, cellular and molecular physiology, neurophysiology, epidemiology, and therapeutic development. By providing free and immediate access to the scientific and Parkinson's disease community, npj Parkinson's Disease promotes collaboration and knowledge sharing among researchers and healthcare professionals.
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