[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122267-en":3,"doc-seo-122267-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122267,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Understanding Parkinson's - The microbiome and machine learning approach","Objective: Enhance Parkinson’s disease diagnosis by combining machine learning with microbiome analysis, aiming to find microbiome signatures that reliably separate patients from healthy controls. Methods: Using four Parkinson-related stool-sample datasets from NCBI, stool microbiome profiles were processed with a DADA2-based workflow and biomarker discovery performed using Recursive Ensemble Feature Selection, validated with an Extra Trees classifier. Results: 84 amplicon sequence variant features achieved >80% discovery accuracy, with ROC-AUC values indicating sufficient to good performance across test datasets. Conclusion: The study identifies transferable microbiome signatures, supporting integration of machine learning and microbiome analysis, while calling for further validation and exploration of therapeutic implications.","Maturitas 193 (2025) 108185  \nContents lists available at ScienceDirect  \nMaturitas  \njournal [homepage: www.elsevier.com/locate/maturitas](homepage: www.elsevier.com/locate/maturitas)  \n| Original article\u003Cbr>Understanding Parkinson's: The microbiome and machine learning approach |  |  |  |\n| --- | --- | --- | --- |\n| David Rojas-Velazquez a,b,*, Sarah Kidwaia, Ting Chia Liua, Mounim A. El-Yacoubie, Johan Garssena,c, Alberto Tondad, Alejandro Lopez-Rincona\u003Cbr>a Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Universiteitsweg 99, Utrecht 3508 TB, the Netherlands\u003Cbr>b Department of Data Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, the Netherlands\u003Cbr>c Global Centre of Excellence Immunology, Danone Nutricia Research, Uppsalalaan 12, Utrecht 3584 CT, the Netherlands\u003Cbr>d UMR 518 MIA-PS, INRAE, Universit'e Paris-Saclay, Institut des Syst'emes Complexes de Paris, Ile-de-France (ISC-PIF) - UAR 3611 CNRS, 113 rueˆNationale, Paris 75013, Paris, France\u003Cbr>e SAMOVAR, Telecom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, Paris, France |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Feature selection Machine learning Biomarker discovery Deep learning |  | Objective: Given that Parkinson's disease is a progressive disorder, with symptoms that worsen over time, our goal is to enhance the diagnosis of Parkinson's disease by utilizing machine learning techniques and microbiome analysis. The primary objective is to identify specific microbiome signatures that can reproducibly differentiate patients with Parkinson's disease from healthy controls.\u003Cbr>Methods: We used four Parkinson-related datasets from the NCBI repository, focusing on stool samples. Then, we applied a DADA2-based script for amplicon sequence processing and the Recursive Ensemble Feature Selection (REF) algorithm for biomarker discovery. The discovery dataset was PRJEB14674, while PRJNA742875, PRJEB27564, and PRJNA594156 served as testing datasets. The Extra Trees classifier was used to validate the selected features.\u003Cbr>Results: The Recursive Ensemble Feature Selection algorithm identified 84 features (Amplicon Sequence Variants) from the discovery dataset, achieving an accuracy of over 80%. The Extra Trees classifier demonstrated good diagnostic accuracy with an area under the receiver operating characteristic curve of 0.74. In the testing phase, the classifier achieved areas under the receiver operating characteristic curves of 0.64, 0.71, and 0.62 for the respective datasets, indicating sufficient to good diagnostic accuracy. The study identified several bacterial taxa associated with Parkinson's disease, such as Lactobacillus, Bifidobacterium, and Roseburia, which were increased in patients with the disease.\u003Cbr>Conclusion: This study successfully identified microbiome signatures that can differentiate patients with Parkinson's disease from healthy controls across different datasets. These findings highlight the potential of integrating machine learning and microbiome analysis for the diagnosis of Parkinson's disease. However, further research is needed to validate these microbiome signatures and to explore their therapeutic implications in developing targeted treatments and diagnostics for Parkinson's disease. |  |\n\n1. Introduction  \nParkinson's disease (PD) is a progressive neurodegenerative disorder that predominantly affects movement. It manifests through symptoms such as tremors, rigidity, bradykinesia (slowness of movement), and postural instability. Additionally, non-motor symptoms like cognitive impairment, mental health disorders, sleep disturbances, and pain are  \ncommon and can significantly impact quality of life [1]. The exact cause of PD remains unknown, but it is thought to result from a combination of genetic and environmental factors. The hallmark of PD is the loss of dopamine-producing","cbCaibpxf3iCq8HF","https://ap.wps.com/l/cbCaibpxf3iCq8HF","pdf",5814787,1,"English","en",105,"# Introduction\n## Parkinson’s disease overview and current diagnosis\n## Role of non-motor symptoms and treatment landscape\n## Motivation for machine learning and AI in PD diagnosis\n## Study challenges and research direction","[{\"question\":\"What is the main goal of this study on Parkinson’s disease?\",\"answer\":\"To improve Parkinson’s diagnosis by using machine learning together with microbiome analysis to identify microbiome signatures that can distinguish patients from healthy controls.\"},{\"question\":\"Which datasets and data type were used for the analysis?\",\"answer\":\"Four Parkinson-related datasets from the NCBI repository were used, focusing on stool samples for microbiome profiling.\"},{\"question\":\"How were biomarker features discovered and validated?\",\"answer\":\"Features were discovered using a Recursive Ensemble Feature Selection approach after DADA2-based amplicon sequence processing, and selected features were validated using an Extra Trees classifier.\"}]","Understanding Parkinson's - 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