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

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.