Genomics is laying the groundwork for a new generation of diagnostics and therapeutics, built on our growing understanding of the biological mechanisms of disease. At the Broad Institute, biologists, chemists, physicians, mathematicians, computational biologists, and software engineers are using systematic, unbiased approaches to identify the root causes of disease and find new opportunities for therapeutic intervention. Learn more about #HowWeScience.
Developing and using computational tools to detect subtle patterns in biomedical data.
Machine learning is an area of artificial intelligence and computer science involving the development of computational tools that can detect subtle patterns and connections in data missed by conventional tools. Researchers create a machine learning model by training it on datasets, allowing it to identify statistical relationships between the data and then using those relationships to make predictions when it crunches through new datasets.
Many researchers at Broad and beyond are harnessing machine-learning algorithms and other computational methods to find patterns in ever-growing troves of biological data, including clinical, genomic, and imaging data. These tools are enabling clinicians, biologists and data scientists to work together to glean new insights into key cellular and molecular processes and how they go awry in disease. Such insights could pave the way to new and better ways of diagnosing and treating disease. Machine learning also allows scientists to tackle challenging problems in biomedicine that previously seemed out of reach.