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

Priya Arora, Ashutosh Mishra, Avleen Malhi*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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.

Original languageEnglish
Number of pages11
JournalNeural Computing & Applications
DOIs
Publication statusE-pub ahead of print - 24 Apr 2021
MoE publication typeA1 Journal article-refereed

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

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