Genomics at the Nexus of AI, Computer Vision, and Machine Learning

23Genetic Variant Classification Through Decision Tree Analysis for Enhanced Genomic Understanding

Prachi Chaudhary^* and Rajni Mehra

Department of Electronics and Communication Engineering, DCRUST, Murthal, India

Abstract

Genomic research has grown immensely, leading to a surge in the discovery of genetic variations. Efficient methods are needed to classify these variants based on their functional significance. This study explores using decision tree analysis as a powerful tool to classify genetic variants, aiming to deepen our grasp of the genomic landscape. We use a comprehensive dataset containing various genetic variants from different genomic databases. We process the data using bioinformatics tools and computational algorithms to identify essential features and build a decision tree model. The model is trained and tested using datasets with known variant classifications to ensure accuracy. The decision tree model effectively sorts genetic variants into meaningful categories like pathogenic, benign, and uncertain. The simplicity of decision trees helps identify key genomic features affecting the classification, shedding light on biological mechanisms. Additionally, we examine how well the decision tree model works across diverse genomic scenarios, such as different genes and functional regions. The results show the model’s reliability and potential for broad use in variant classification. This research contributes to ongoing efforts to simplify and automate genetic variant ...