Ramya Sriram, digital content manager at Kolabtree explains how advances in big data are enabling genomics to thrive in pharma.
Big Data
Out of the three billion pairs of DNA molecules that make up the human genome, only 0.1% are unique to each person. To find patterns useful to medical research in such a vast sea of data, researchers can benefit from tools that cut through the noise. Fortunately, these are the same problems that big data has been trying to solve for almost a decade.
Biotechnology, broadly encompassing technological applications using biological systems, finds itself in similar conditions to those that allowed big data to emerge. As the volume and variety of medical data grows, it becomes more advantageous to refine it into information that can be used to make decisions. In 2019, Nature published a paper exploring the current state of machine learning (ML) in drug discovery and development. Because of the vast quantity of patient data now readily available, ML has now become a valuable tool to model the behaviour of drugs and find potential applications for existing and new molecules. Big data is not just in the future of the pharmaceutical industry, it is shaping its present.