Integrating DL Techniques for Precise Genome Mapping Methods and Applications

In the field of genetic understanding of human traits and disorders, Genome Mapping (GM) is a major part to comprehend genetic variations, disease mechanisms, and personalized medicine. Such challenges are faced by traditional methods that are not able to map genomes correctly and quickly due to a large amount of genomic data which is complex and complicated. During recent years, Deep Learning (DL) techniques have become very popular as well as promising tools to address these challenges. The next section provides an overview of how DL can be integrated into GM for accurate mapping, such as DL integration approaches and their applications. In the first place, the chapter addresses basics of GM as well as drawbacks associated with conventional methods. Starting from its applications to genomic data analysis, we explain the DL architecture. Further on, different DL architectures such as CNNs and RNNs that are specialized for GM tasks are elaborated. Additionally, it presents the strategies of data preprocessing, feature extraction, and model optimization with the goal of improving mapping accuracy and efficiency. A study was conducted where real-world applications of DL in GM such as variant calling, structural variant detection, and regulatory element prediction were discussed. Furthermore, it enumerates present issues and possibilities for the future while underscoring that genomic research and precision medicine would be impossible without interdisciplinary teamwork and data union. Taken together, this chapter offers a rich set of learning materials on the utilization of deep learning methods in accurate GM and its consequences for clinical therapy and biological studies.