Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder
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 m...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-04-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.00226/full |
| _version_ | 1811312405647458304 |
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| author | Jiajie Peng Jiaojiao Guan Xuequn Shang |
| author_facet | Jiajie Peng Jiaojiao Guan Xuequn Shang |
| author_sort | Jiajie Peng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T10:35:39Z |
| format | Article |
| id | doaj.art-269d1ed21a194790ab5dd37bdc979623 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-13T10:35:39Z |
| publishDate | 2019-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-269d1ed21a194790ab5dd37bdc9796232022-12-22T02:50:03ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-04-011010.3389/fgene.2019.00226441295Predicting Parkinson's Disease Genes Based on Node2vec and AutoencoderJiajie PengJiaojiao GuanXuequn ShangIdentifying 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.https://www.frontiersin.org/article/10.3389/fgene.2019.00226/fullPPI networkParkinson's diseasedeep learningnode2vecfeature representation |
| spellingShingle | Jiajie Peng Jiaojiao Guan Xuequn Shang Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder Frontiers in Genetics PPI network Parkinson's disease deep learning node2vec feature representation |
| title | Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder |
| title_full | Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder |
| title_fullStr | Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder |
| title_full_unstemmed | Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder |
| title_short | Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder |
| title_sort | predicting parkinson s disease genes based on node2vec and autoencoder |
| topic | PPI network Parkinson's disease deep learning node2vec feature representation |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.00226/full |
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