Design of an Efficient Model for Detecting Diseases Using Genomics with Graph Relationship Networks

This paper introduces novel approaches utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Autoencoders, and Graph Neural Networks (GNNs) in genomics. As the existing methods, while valuable, have limitations that hinder their effectiveness and often struggle with precision, accuracy, and speed in genome classification, leading to suboptimal results. Moreover, their inability to efficiently analyze multiple omics data and non-coding DNA limits our understanding of complex disease mechanisms. In contrast, our proposed models harness the power of CNNs for identifying disease-related genetic markers, achieving automatic feature detection, reducing bias, and enhancing accuracy. Additionally, the utilization of RNNs, specifically Long Short-Term Memory (LSTM) networks, enables the precise annotation of genomic variants by understanding long-term dependencies in genetic sequences. Deep Autoencoders facilitate comprehensive disease analysis and Graph Neural Networks (GNNs) delve into non-coding DNA, revealing intricate regulatory mechanisms and functions previously hidden. Our models exhibit a remarkable 9.5% improvement in genome classification precision, 8.5% higher accuracy, 6.5% enhanced recall, 8.3% increased speed, and a 10.5% superior Area Under the Curve (AUC) when compared to existing methods. This paper represents a significant step forward in genomics research, paving the way for more precise diagnostics and treatments for genetic diseases.