ABSTRACT
In the era of biomedical research, the evolving field of omics caused an expansion in molecular data. Both clinical and medical imaging data sets enabled omics techniques to adopt precision medicine (PM) for medical care. In emerging fields of omics and biomedical research, holistic data sets are diverse and complicated for analyzing and retrieving useful information from wide-ranging data sets. The development of potential PM-related tasks is a big challenge for machine learning (ML) investigators among computational fields of bioinformatics and other biomedical sciences. Therefore, immense development of technology in the field of medical science, deep learning (DL) techniques provides promising contexts to contribute toward resolving these challenges via establish analytic frameworks and recognize complicated trends in wide-ranging data sets. This chapter will summarize the entry-level guideline for appropriate researchers to understand the major DL tools, techniques, models, methods, and their broad-ranging applications in biomedical studies for PM which have joint mechanisms of omics and deep learning networks (DLN). Finally, according to the recent mainstream, we discuss precise scenarios and improved decision-making approaches to enhance the process of detecting, sorting, and managing complex disorders in order of omics.
