Abstract
The last two decades have witnessed unprecedented advancements in computational techniques and artificial intelligence. These new developments will greatly impact biological data analyses for the health care system. The availability of large scale, high-throughput, biomedical data sets offers fertile ground for the application of AI-based techniques to extract valuable information that can be harnessed in the diagnosis and treatment of disease. This chapter provides a comprehensive review of the computational tools and online resources for the high-throughput analysis of biomedical data. It focuses on single-cell RNA sequencing data, multi-omics data integration, AI drug design, medical imaging data analysis, and the IoT. After providing a brief overview of fundamental biological terms, we describe a variety of research problems in the health care system and how high-throughput data can help. Next, we provide an in depth overview of machine learning techniques in computing and learning methods that can be used in sequencing data analyses (including profiles of traditional mass sequencing, single-cell data, interaction data, line-cell drugs, and medical imaging data). Finally, applications of IoT methods are discussed for biomedical research in detail. In conclusion, the review delineates the overall scope of AI-based computational techniques in biomedical research.