Article: Diagnosis of Parkinson's disease genes using LSTM and MLP-based multi-feature extraction methods Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2023 Vol.27 No.4 pp.326 - 348 Abstract: Disease gene identification using computational methods is one of the most challenging issues to improve the treatment and diagnosis of Parkinson's disease (PD). Various intelligent computing techniques have been introduced to predict disease associated genes but the major difference among these approaches is in the data type to be used to create a feature vector. In this paper, deep learning methods such as multi-layer perceptron (MLP) and long short-term memory (LSTM) are adopted to identify genes that are responsible for Parkinson's disease. The proposed method has been optimised on the bases of: 1) amino acid's physicochemical properties to construct a feature vector; 2) feature extraction method to reduce the effect of noise and to speed up the process; 3) the genes prediction is done by employing deep learning methods. Compared with different type of datasets and other classifiers, the proposed method improves the prediction performance of neurodegenerative diseases. The experimental results indicate that the proposed deep learning approach outperforms the existing gene identification methods with higher recall, precision and F-score of 88.2, 84.5 and 85.0, respectively. The results of proposed system indicate the efficiency and accuracy for Parkinson's disease gene identification and classification. Inderscience Publishers - linking academia, business and industry through research

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Title: Diagnosis of Parkinson's disease genes using LSTM and MLP-based multi-feature extraction methods

Authors: Priya Arora; Ashutosh Mishra; Avleen Malhi

Addresses: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India ' Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India ' Data Science and AI, Bournemouth University, UK

Abstract: Disease gene identification using computational methods is one of the most challenging issues to improve the treatment and diagnosis of Parkinson's disease (PD). Various intelligent computing techniques have been introduced to predict disease associated genes but the major difference among these approaches is in the data type to be used to create a feature vector. In this paper, deep learning methods such as multi-layer perceptron (MLP) and long short-term memory (LSTM) are adopted to identify genes that are responsible for Parkinson's disease. The proposed method has been optimised on the bases of: 1) amino acid's physicochemical properties to construct a feature vector; 2) feature extraction method to reduce the effect of noise and to speed up the process; 3) the genes prediction is done by employing deep learning methods. Compared with different type of datasets and other classifiers, the proposed method improves the prediction performance of neurodegenerative diseases. The experimental results indicate that the proposed deep learning approach outperforms the existing gene identification methods with higher recall, precision and F-score of 88.2, 84.5 and 85.0, respectively. The results of proposed system indicate the efficiency and accuracy for Parkinson's disease gene identification and classification.

Keywords: disease gene; deep learning models; feature extraction; physicochemical properties of amino acid; Parkinson's disease.

DOI: 10.1504/IJDMB.2023.134301

International Journal of Data Mining and Bioinformatics, 2023 Vol.27 No.4, pp.326 - 348

Received: 14 Apr 2023
Accepted: 19 Jun 2023
Published online: 17 Oct 2023 *

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Title: Diagnosis of Parkinson's disease genes using LSTM and MLP-based multi-feature extraction methods

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