Identification Of Gene Variant Associated With Parkinson’s Disease Using Genomic Databases

Author(s)	Prof. Karpagavalli K, Ms. Sona P, Ms. Yoga Lakshmi N, Ms. Yuvasri E
Country	India
Abstract	Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that severely impacts motor and non-motor functions. This study aims to identify gene variants associated with PD using genomic databases and bioinformatics tools. Leveraging databases such as NCBI and Ensembl, and applying machine learning algorithms like Logistic Regression and Random Forest, the project classifies gene variants as pathogenic or benign. Feature selection is based on parameters like clinical significance, variant frequency, and pathogenicity scores (SIFT, PolyPhen, CADD). This work provides a foundation for improving early diagnosis, personalizing treatments, and understanding the genetic basis of PD.
Keywords	Parkinson’s Disease, Genomic Databases, Gene Variants, Machine Learning, Logistic Regression, Random Forest
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-04
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.42599
Short DOI	https://doi.org/g9hscn

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International Journal For Multidisciplinary Research