A Computational approach for the identification of Parkinson’s Disease Gene

Abstract

Parkinson's disease is a serious brain disorder which affects the nervous system and coordination. Genetic and environmental factors might have an involvement in the causing of this syndrome but the true causes and the symptoms are still not predictable. Therefore, early detection and diagnosis can be done only using the identification of disease genes. These disease gene identifications can be done using different computational approaches.  The proposed system is a prediction model to detect Parkinson’s disease gene from the normal genes using isolation forest algorithm.