The Era of Accuracy Medicine in Biomedical Healthcare Genomic Analysis Using Deep Learning

Abstract

Biomedical genomic analysis, powered by deep learning methodologies, has emerged as a transformative paradigm in healthcare. The integration of deep learning techniques with genomic data has opened new avenues for understanding complex biological processes, enabling personalized medicine, and advancing disease diagnosis and treatment strategies. This abstract provides a concise overview of the key aspects and implications of applying deep learning to biomedical genomic analysis. The utilization of deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transfer learning techniques, has enabled the extraction of intricate patterns from diverse genomic datasets. These models contribute to the identification of genetic markers, disease susceptibility factors, and pathways associated with various health conditions, particularly in cancer detection, rare disease diagnosis, and drug discovery. Furthermore, deep learning facilitates the prediction of clinical outcomes based on genomic profiles, aiding clinicians in tailoring treatment plans for individual patients. The integration of multi-omics data, encompassing genomic, transcriptomic, and epigenomic information, allows for a comprehensive understanding of biological processes and regulatory networks.