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Understanding Parkinson's: The microbiome and machine learning approach
David Rojas-Velazquez
*
,
Sarah Kidwai
, Ting Chia Liu
, Mounim A. El-Yacoubi
,
Johan Garssen
, Alberto Tonda
,
Alejandro Lopez-Rincon
*
Corresponding author for this work
Pharmacology
Pharmacology
Utrecht University
Telecom & Management SudParis
Ile-de-France (ISC-PIF) - UAR 3611 CNRS
Research output
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Keyphrases
Parkinson's Disease
100%
Microbiome
100%
Parkinson
100%
Machine Learning Approach
100%
Microbial Signatures
60%
Patients with Parkinson's Disease
40%
Healthy Controls
40%
Diagnostic Accuracy
40%
Area under the Receiver Operating Characteristic Curve
40%
Microbiological Analysis
40%
Feature Selection Methods
40%
Ensemble Feature Selection
40%
Extra Tree Classifier
40%
Different Datasets
20%
Lactobacillus
20%
Stool Samples
20%
Bacterial Taxa
20%
Biomarker Discovery
20%
Targeted Treatment
20%
Over 80
20%
Bifidobacterium
20%
Machine Learning Techniques
20%
Amplicon Sequencing
20%
Machine Learning Analysis
20%
Progressive Disease
20%
Therapeutic Implications
20%
Technique Analysis
20%
Roseburia
20%
Sequence Processing
20%
Selected Features
20%
DADA2
20%
Amplicon Sequence Variants
20%
Computer Science
Machine Learning Approach
100%
Parkinson's Disease
100%
Diagnostic Accuracy
28%
Healthy Control
28%
Characteristic Curve
28%
Feature Selection
28%
Feature Extraction
28%
Machine Learning Technique
14%
Primary Objective
14%
Machine Learning
14%
Learning System
14%
Biochemistry, Genetics and Molecular Biology
Microbiome
100%
Amplicon
33%
Feature Extraction
33%
Biomarker Discovery
16%
Taxon
16%
Bifidobacterium
16%
Lactobacillus
16%
Roseburia
16%
Immunology and Microbiology
Microbiome
100%
Amplicon
33%
Feature Extraction
33%
Taxon
16%
Lactobacillus
16%
Bifidobacterium
16%
Roseburia
16%
Neuroscience
Parkinson's Disease
100%
Amplicon
40%
Diagnosis of Parkinson's Disease
40%
Material Science
Bifidobacteria
100%