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Predicting Parkinson’s Disease Related Genes Based on PyFeat and Gradient Boosted Decision Tree (GBDT)


 Identifying genes related to Parkinson's disease (PD) is an active and effective research topic in biomedical analysis, which plays a critical role in diagnosis and treatment. In recent years, many studies have proposed different techniques for predicting disease-related genes. However, a few of these techniques are designed or developed for PD gene prediction. Most of these PD techniques are developed to identify only protein genes and discard long non-coding (lncRNA) genes, which play an essential role in biological processes and the Transformation and development of diseases. This paper proposes a novel prediction system to identify protein and lncRNA genes related to PD that can aid in an early diagnosis. First, we preprocessed the genes into DNA FASTA sequences from the UCSC genome browser and removed the redundancies. Second, we extracted some significant features of DNA FASTA sequences using five numerical mapping techniques with Fourier transform and PyFeat method with Adaboost technique as feature selection. Finally, the features were fed to the gradient boosted decision tree (GBDT) to diagnose different tested cases. Seven performance metrics are used to evaluate the performance of the proposed system. The proposed system achieved an average accuracy (ACC) equals 78.1%, the area under the curve (AUC) equals 84.9%, the area under precision-recall (AUPR) equals 85.0%, F1-score equals 78.2%, Matthews correlation coefficient (MCC) equals 0.564, Sensitivity (SEN) equals 79.1%, and specificity (SPC) equals 77.1%. The experiments demonstrate promising results compared with other systems. The predicted top-rank protein and lncRNA genes are verified based on a literature review.