Leveraging Deep Learning and Random Forest Algorithms for AI-Driven Genomics in Personalized Medicine

Abstract

This research paper explores the integration of deep learning and random forest algorithms as a comprehensive AI-driven approach to advance genomics in personalized medicine. The study leverages the strengths of both techniques: the ability of deep learning to identify intricate patterns in large-scale genomic data, and the proficiency of random forests in providing interpretable predictions based on these patterns. A hybrid model is developed, utilizing convolutional neural networks (CNNs) to process raw genomic sequences, followed by random forest classifiers to enhance decision-making through feature importance analysis. The model is trained and validated on diverse genomic datasets, demonstrating superior predictive performance compared to traditional methods. This approach enables the identification of novel genetic markers associated with disease susceptibility and drug response, thereby facilitating the development of tailored therapeutic strategies. Our results indicate a significant increase in the accuracy of patient stratification in cancer genomics and pharmacogenomics, underscoring the potential of these AI technologies to revolutionize personalized medicine. Additionally, the paper discusses the interpretability of random forests as a key factor in overcoming the "black box" challenge often associated with deep learning, thereby enhancing the clinical applicability of AI solutions. This study provides insights into the practical implementation of AI in genomics, emphasizing the need for robust, interpretable models in the pursuit of precision healthcare.