N-semble-based method for identifying Parkinson's disease genes
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Journal Title
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Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2021-04-24
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
Series
Neural Computing & Applications
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’, 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
Citation
Arora , P , Mishra , A & Malhi , A 2021 , ' N-semble-based method for identifying Parkinson's disease genes ' , Neural Computing & Applications . https://doi.org/10.1007/s00521-021-05974-z
