Chapter 11 - Translational bioinformatics: An introduction

Abstract

Translational bioinformatics is an emerging field of bioinformatics that explores its clinical applications. It culminates from the convergence of big data in health care, big molecular data, omics data, biostatistics, and artificial intelligence-based technologies. Bioinformatics knowledge bases and tools are used for building disease models, disease prediction models, drug designing, etc. Translational bioinformatics makes use of data mining and data analysis techniques for interpretation of clinical data. It is a cross-disciplinary approach that employs computer-aided techniques for building information systems that contribute to improve health care. In recent years, this field has drawn much attention toward itself because of the integration of artificial intelligence-based technologies. The synergic integration of data collected from laboratory experiments and correlating it with clinical data is a promising approach to fast and improved health care. In this chapter, we try to establish a link between data-driven research and data-driven clinical routine. This chapter covers criteria for selection of data, efficient data mining, biomedical knowledge integration and mining electronic health records, data-driven outlook on disease biology, drug discovery and pharmacogenomics, biomarker discovery, precision medicine, and incorporation of artificial intelligence to translational bioinformatics.