Machine Learning-Driven Integration of Genetic and Textual Data for Enhanced Genetic Variation Classification

Abstract

Precision medicine and genetic testing have the potential to revolutionize disease treatment by identifying driver mutations crucial for tumor growth in cancer genomes. However, clinical pathologists face the time-consuming and error-prone task of classifying genetic variations using Textual clinical literature. In this research paper, titled "Machine Learning-Driven Integration of Genetic and Textual Data for Enhanced Genetic Variation Classification", we propose a solution to automate this process. We aim to develop a robust machine learning algorithm with a knowledge base foundation to streamline precision medicine. Our methods leverage advanced machine learning and natural language processing techniques, coupled with a comprehensive knowledge base that incorporates clinical and genetic data to inform mutation significance. We use text mining to extract relevant information from scientific literature, enhancing classification accuracy. Our results demonstrate significant improvements in efficiency and accuracy compared to manual methods. Our system excels at identifying driver mutations, reducing the burden on clinical pathologists and minimizing errors. Automating this critical aspect of precision medicine promises to empower healthcare professionals to make more precise treatment decisions, advancing the field and improving patient care.