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

Thumbnail Image

Access rights

openAccess
publishedVersion

URL

Journal Title

Journal ISSN

Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)

Authors

Date

2023-11

Department

Department of Computer Science

Major/Subject

Mcode

Degree programme

Language

en

Pages

11

Series

Neural Computing & Applications, Volume 35, issue 33, pp. 23829–23839

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.

Description

Keywords

Parkinson&#8217, s disease, Machine learning methods, Healthcare, Physicochemical properties of amino acid, Neural networks, PROTEIN-PROTEIN INTERACTIONS, TOPOLOGICAL FEATURES, NEURAL-NETWORK, PREDICTION, IDENTIFICATION, AUTOCORRELATION, CLASSIFICATION, SIMILARITY, SURFACE

Other note

DOI

10.1007/s00521-021-05974-z

Citation

Arora, P, Mishra, A & Malhi, A 2023, 'N-semble-based method for identifying Parkinson's disease genes', Neural Computing & Applications, vol. 35, no. 33, pp. 23829–23839. https://doi.org/10.1007/s00521-021-05974-z

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202106027139

Collections

[article-cris] Perustieteiden korkeakoulu / SCI