Poster - Thur Eve - 03: Application of the non-negative matrix factorization technique to [11 C]-DTBZ dynamic PET data for the early detection of Parkinson's disease

2014 ◽  
Vol 41 (8Part2) ◽  
pp. 7-7
Author(s):  
Dong-Chang Lee ◽  
Hans Jans ◽  
Wayne Martin ◽  
Marguerite Wieler ◽  
Sandy McEwan ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Matrix Factorization ◽  
Dynamic Pet ◽  
Factorization Technique ◽  
Matrix Factorization Technique ◽  
Non Negative Matrix Factorization

scholarly journals Clustering of Alzheimer’s and Parkinson’s disease based on genetic burden of shared molecular mechanisms

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mohammad Asif Emon ◽  
Ashley Heinson ◽  
Ping Wu ◽  
Daniel Domingo-Fernández ◽  
Meemansa Sood ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Matrix Factorization ◽  
Neurological Disorders ◽  
Molecular Mechanisms ◽  
Molecular Level ◽  
Molecular Characteristics ◽  
Genetic Burden ◽  
Patient Subgroups ◽  
Non Negative Matrix Factorization

Abstract One of the visions of precision medicine has been to re-define disease taxonomies based on molecular characteristics rather than on phenotypic evidence. However, achieving this goal is highly challenging, specifically in neurology. Our contribution is a machine-learning based joint molecular subtyping of Alzheimer’s (AD) and Parkinson’s Disease (PD), based on the genetic burden of 15 molecular mechanisms comprising 27 proteins (e.g. APOE) that have been described in both diseases. We demonstrate that our joint AD/PD clustering using a combination of sparse autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with significant differences of AD and PD patient subgroups on a clinical, pathophysiological and molecular level. Hence, clusters are disease-associated. To our knowledge this work is the first demonstration of a mechanism based stratification in the field of neurodegenerative diseases. Overall, we thus see this work as an important step towards a molecular mechanism-based taxonomy of neurological disorders, which could help in developing better targeted therapies in the future by going beyond classical phenotype based disease definitions.

scholarly journals Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 147635-147646 ◽  
Author(s):  
Wu Wang ◽  
Junho Lee ◽  
Fouzi Harrou ◽  
Ying Sun
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Early Detection

scholarly journals Augmenting matrix factorization technique with the combination of tags and genres

2016 ◽  
Vol 461 ◽  
pp. 101-116 ◽  
Author(s):  
Tinghuai Ma ◽  
Xiafei Suo ◽  
Jinjuan Zhou ◽  
Meili Tang ◽  
Donghai Guan ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Factorization Technique ◽  
Matrix Factorization Technique

Early Detection of Parkinson's Disease

2019 ◽  
pp. 61-95
Author(s):  
Debashree Devi ◽  
Saroj K. Biswas ◽  
Biswajit Purkayastha
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Detection ◽  
Neurodegenerative Disorder ◽  
Diagnostic System ◽  
Diagnostic Model ◽  
Neural Signals ◽  
Learning Machine ◽  
Preprocessing Technique ◽  
Thalamic Region

Parkinson's disease (PD) is a neurodegenerative disorder that occurs due to corrosion of the substantia nigra, located in the thalamic region of the human brain, and is responsible for transmission of neural signals throughout the human body by means of a brain chemical, termed as “dopamine.” Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Through this chapter, an intelligent diagnostic system is proposed by integrating one-class SVM, extreme learning machine, and data preprocessing technique. The proposed diagnostic model is validated with six existing techniques and four learning models. The experimental results prove the combination of proposed method with ELM learning model to be highly effective in case of early detection of Parkinson's disease, even in presence of underlying data issues.

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