Journal
FRONTIERS IN GENETICS
Volume 10, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2019.00226
Keywords
PPI network; Parkinson's disease; deep learning; node2vec; feature representation
Categories
Funding
- National Natural Science Foundation of China [61702421, U1811262, 61772426]
- international Postdoctoral Fellowship Program [20180029]
- China Postdoctoral Science Foundation [2017M610651]
- Fundamental Research Funds for the Central Universities [3102018zy033]
- Top International University Visiting Program for Outstanding Young scholars of Northwestern Polytechnical University
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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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