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Expansion of scientific data in cancer has created enormous opportunity in personalizing treatment choices for patients. However, given that humans can only process about 5-8 variables at any given time, the copious amount of data challenges our ability to discern and direct the most appropriate treatment choices.  Examples include the use of genetic panels to help screen patients for specific treatments or clinical trials.  Furthermore, data from electronic health records (EHR) have been shown to increase opportunities to enhance and optimize decision making. With the emergence of statistical learning, artificial intelligence tasks in oncology such as workflow optimization, detection of clinical trial eligibility and risk stratification can be developed to provide decision support using historical data.  

To this end, our consortium is developing

(I) a MEDomics framework which is at the core of

(II) an open-source MEDomicsLab multi-omics computation platform and novel algorithms to detect/understand/predict a number of end-points in the treatment of diverse cancers.

MEDomics Video