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International Journal of Bioinformatics and Computational Biology Review Article

Bioinformatics and Medicine: Bringing Data to the Bedside

  1. Rajat Kulshrestha 4Gen.Bio, Bengaluru, Karnataka, India
  2. V. G. Shanmuga Priya Department of Life Sciences, School of Sciences, Garden City University, Bengaluru, Karnataka, India

Abstract

From being an empirical and experience-based practice, modern medicine has transformed into a codified and research-based discipline, known as Evidence-Based Medicine (EBM). Though EBM has greatly enhanced the quality of medical practice through population-scale clinical trials, it still has limitations in managing biologically diverse patient populations, especially when dealing with clinical outliers who respond in an unusual way to standard treatments. With the rapid progress in genomics, proteomics, and high-throughput technologies, there has been an unprecedented surge in biological and medical data, providing new avenues for further refinement in diagnosis, prognosis, and therapy selection in individual patients. Clinical bioinformatics has emerged as an important translational discipline to fill the gap between bench and bedside, and its application in conjunction with genomic variations, biomarkers, pharmacogenomics, and Electronic Health Records (EHRs) has paved the way for the transition to Personalized Precision Medicine (PPM), in which preventive and therapeutic approaches are tailored to individual biological characteristics. The increasing trend in big data from high-throughput technologies, medical images, and wearable devices has further enhanced predictive modeling and patient stratification, although concerns regarding data quality, compatibility, and governance issues still need to be addressed. Large-scale collaborative projects in genomics and infrastructure development are beginning to address these issues through large-scale analysis without compromising patient confidentiality. Clinical translation also requires infrastructure development, guidelines, and a clinical bioinformatician workforce proficient in interpreting complex genomic data and able to communicate this to clinicians. This article will highlight the evolution of EBM to PPM, how bioinformatics facilitates this evolution, current challenges in its implementation, and future requirements in developing a predictive data-driven framework in clinical practice.

Keywords

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