A Neurodevelopmental Origin for Pakinson's Disease: A Link to the Fetal Basis for Adult Disease Hypothesis

Urbach-Ross, Daniella; Thiruchelvam, Mona

doi:10.1002/9780470917060.ch13

Cited by 1 publication

(1 citation statement)

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“…The exact causes of this pathological change are still unclear. Genetic factors, environmental factors, aging, and oxidative stress may be involved in the degenerative death of PD dopaminergic neurons (Urbach-Ross and Thiruchelvam, 2010). Studies have shown that the occurrence of human diseases is rarely caused by a single gene, and most diseases are related to multiple genes (Barabási et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder

2019

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Identifying genes associated with Parkinson's disease plays an extremely important role in the diagnosis and treatment of Parkinson's disease. In recent years, based on the guilt-by-association hypothesis, many methods have been proposed to predict disease-related genes, but few of these methods are designed or used for Parkinson's disease gene prediction. In this paper, we propose a novel prediction method for Parkinson's disease gene prediction, named N2A-SVM. N2A-SVM includes three parts: extracting features of genes based on network, reducing the dimension using deep neural network, and predicting Parkinson's disease genes using a machine learning method. The evaluation test shows that N2A-SVM performs better than existing methods. Furthermore, we evaluate the significance of each step in the N2A-SVM algorithm and the influence of the hyper-parameters on the result. In addition, we train N2A-SVM on the recent dataset and used it to predict Parkinson's disease genes. The predicted top-rank genes can be verified based on literature study.

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Section: Introductionmentioning

confidence: 99%