Enhancing Hypertrophic Cardiomyopathy Heart Risk Assessment with Deep Learning and Multi-Omics Data Integration

This research explores the possibility of improving risk assessment for hypertrophic cardiomyopathy (HCM) by combining multi-omics data and deep learning methodologies. In this analysis, the research included 513 patients and established prediction models based on genomics, transcriptomics, and proteomics data. The research also used several deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTM), and graph neural network solvers. The notable results were that the CNN model's accuracy was 93%, with corresponding precision and recall rates of 91% and 92%, respectively. In addition, the performance of the models was increased when combining different omics data hierarchically. CNNs showed improvements in accuracy from 85% using single-omics to 92% with integrated multi-omics. Revealing the intricate biological relationships associated with Hypertrophic cardiomyopathy (HCM) necessitates high-quality, comprehensive data integration. These insights not only contribute to a more comprehensive understanding of HCM's pathophysiology but also serve as seeds for the discovery of novel biomarkers and new therapeutic targets. In summary, this work demonstrates the power of multi-omics and deep learning approaches for developing precision medicine in HCM with applications to improved risk stratification and patient management strategies.