Abstract
Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and to improve routine medical and public health practice. Understanding patients' multi-omics make-up in conjunction with the clinical data will lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and to optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. Precision medicine promotes integrating collective and individualized clinical data with patient-specific multi-omics data to develop therapeutic strategies and knowledge bases for predictive and personalized medicine in diverse populations. Artificial intelligence approaches and machine learning algorithms will add additional capabilities to precision medicine that will leverage and extend the information contained within the original data and facilitate modeling patient-specific multi-omics data against publicly available annotation data for better understanding disease mechanisms. This chapter discusses emerging, significant, and recently reported multi-omics, deep phenotyping, and translational approaches to facilitate the implementation of precision medicine, as well as innovative, smart, and robust big-data platforms that are necessary to improve the quality and transition of healthcare by analyzing heterogeneous healthcare and multi-omics data.