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N-semble-based method for identifying Parkinson's disease genes.

Arora, P., Mishra, A. and Malhi, A., 2021. N-semble-based method for identifying Parkinson's disease genes. Neural Computing and Applications. (In Press)

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DOI: 10.1007/s00521-021-05974-z

Abstract

Parkinson’s disease (PD) genes identification plays an important role in improving the diagnosis and treatment of the disease. A number of machine learning methods have been proposed to identify disease-related genes, but only few of these methods are adopted for PD. This work puts forth a novel neural network-based ensemble (n-semble) method to identify Parkinson’s disease genes. The artificial neural network is trained in a unique way to ensemble the multiple model predictions. The proposed n-semble method is composed of four parts: (1) protein sequences are used to construct feature vectors using physicochemical properties of amino acid; (2) dimensionality reduction is achieved using the t-Distributed Stochastic Neighbor Embedding (t-SNE) method, (3) the Jaccard method is applied to find likely negative samples from unknown (candidate) genes, and (4) gene prediction is performed with n-semble method. The proposed n-semble method has been compared with Smalter’s, ProDiGe, PUDI and EPU methods using various evaluation metrics. It has been concluded that the proposed n-semble method outperforms the existing gene identification methods over the other methods and achieves significantly higher precision, recall and F Score of 88.9%, 90.9% and 89.8%, respectively. The obtained results confirm the effectiveness and validity of the proposed framework.

Item Type:Article
ISSN:0941-0643
Uncontrolled Keywords:Parkinson’s disease; Machine learning methods; Healthcare; Physicochemical properties of amino acid; Neural networks
Group:Faculty of Science & Technology
ID Code:35614
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:08 Jun 2021 09:41
Last Modified:15 Aug 2021 08:29

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