Approaches for the integration of big data in translational medicine: single-cell and computational methods

Translational medicine describes a bench-to-bedside approach that eventually converts findings from basic scientific studies into real-world clinical research. It encompasses new treatments, advanced equipment, medical procedures, preventive and diagnostic approaches creating a bridge between basic studies and clinical research. Despite considerable investment in basic science, improvements in technology, and increased knowledge of the biology of human disease, translation of laboratory findings into substantial therapeutic progress has been slower than expected, and the return on investment has been limited in terms of clinical efficacy. In this review, we provide a fresh perspective on some experimental and computational approaches for translational medicine. We cover the analysis, visualization, and modeling of high-dimensional data, with a focus on single-cell technologies, sequence, and structure analysis. Current challenges, limitations, and future directions, with examples from cancer and fibrotic disease, will be discussed.