Big data holds the promise to transform industries and research, and spawn new solutions to a range of challenges in healthcare and the life sciences. With massive scalability, breakthrough economics, and a vibrant ecosystem, big data platforms enable the capture of diverse data at very high volumes and velocities. Yet, achieving the anticipated data insights remains elusive. Data scientists are scarce, and their efforts are often taxed by the effort and complexity required to program and integrate the diversity of advanced, open-source analytics tools. The typical data science workflow is not conducive to iteration and collaboration, further slowing time to insight. And, finally, many of the tools data scientists use, focus on answering known questions, sometimes on sampled data, at the expense of opportunities to use big data to discover answers to questions no one has thought to ask.
Intel and the Icahn School of Medicine at Mount Sinai have initiated a project called DrugGraph to explore the applicability of data science advances to therapeutic drug discovery, using data science capabilities found in the open source software project Trusted Analytics Platform (TAP) to greatly reduce the complexity of big data analytics processes. The project employs hardware and software advances in data science, including big data analytics, graph analytics, and machine learning, to help predict the clinical efficacy of existing drugs and compounds, and make the management and discovery of drug-related information more efficient. Success in this joint research can lead to faster, less expensive discovery of new drug therapies; improved patient outcomes by reducing toxic drug reactions; and reduced cost of treatment by predicting novel uses for existing compounds.
Using TAP, data scientists can achieve efficient knowledge discovery and modeling on big data. TAP provides data scientists with extensible tools, scalable algorithms and powerful engines to train and deploy predictive models.
This paper will explore the building of DrugGraph, the scientific challenges in doing so, and the use of TAP.
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