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Smart, Precision and Preventive Medicine

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(Eiffel Tower, Paris, France - Hsi-Pin Ma)


"Precision medicine aims to collect, connect, and apply vast amounts of scientific research data and information about our health to understand why individuals respond differently to treatments and therapies, and help guide more precise and predictive medicine worldwide." -- [Precision Medicine at UCSF]

Big data technologies are increasingly used for biomedical and healthcare informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of healthcare.
To address the challenges of big data, innovative technologies are needed. Parallel, distributed computing paradigms, scalable machine learning algorithms, and real-time querying are key to analysis of big data. Distributed file systems, computing clusters, cloud computing, and data stores supporting data variety and agility are also necessary to provide the infrastructure for processing of big data. Workflows provide an intuitive, reusable, scalable and reproducible way to process big data to gain verifiable value from it in and enable application of same methods to different datasets.


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