Leveraging Deep Learning and Random Forest Algorithms for Enhanced Genomic Analysis in Rare Disease Identification

Abstract

This research paper explores the integration of deep learning and random forest algorithms to advance genomic analysis for the identification of rare diseases. Amid the burgeoning volume of genomic data, efficient and accurate computational tools are crucial for unlocking insights into rare genetic disorders. This study proposes a hybrid framework that combines the feature extraction capabilities of deep learning with the decision-making efficiency of random forest algorithms, aiming to enhance predictive accuracy and interpretability in rare disease genomics. The methodology involves the application of convolutional neural networks (CNNs) for hierarchical feature extraction from genomic sequences, followed by the utilization of random forests to perform classification tasks based on these features. The proposed approach is validated using publicly available genomic datasets, demonstrating superior performance in terms of accuracy, sensitivity, and specificity compared to traditional single-model approaches. Additionally, the study provides insights into the biological significance of features identified by the model, offering a mechanism for hypothesis generation in rare disease research. This research underscores the potential of hybrid machine learning solutions in genomics, paving the way for more effective diagnostic tools and personalized medicine applications in rare disease identification.