Data Mining and Predictive Analytics – Research Applications

Special thanks to Bob Dunkle for providing information about this event: San Francisco State University Center for Computing for Life Sciences (CCLS) Presents: Data Mining and Predictive Analytics – Research Applications 6:00 PM October 13, 2011 SFSU About the seminar/panel discussion This is the third seminar of the new CCLS Bay Area Seminar Series. This seminar, in the format of a panel discussion, will provide insights and explore issues regarding the role of data mining and predictive analytics in life sciences with a focus on research applications whereas the prior seminar focused on clinical applications. The panelists will describe the evolution of their experiences with data mining and predictive analytics and how they are using these research processes today. The seminar will address how data can be 1.) aggregated and mined, 2.) applied to heuristics, algorithms, and machine learning and then 3.) characterize interrelated data, interpret results and predict endpoints being studied. This seminar will be useful for statisticians, software developers, machine learning researchers, IT architects and information systems managers in life sciences. Please register at: http://sfsudmparesear... Panelists • Nigel Duffy – Chief Technology Officer, Numerate • Pek Lum – VP of Life Sciences, Ayasdi • Nicholas Tatonetti – Biomedical Informatics, Stanford • Thomas Wu – Sr. Scientist, Bioinformatics and Computational Biology, Genentech • Moderator: Bob Dunkle – A.B.E.S Partners Logistics Date: Thursday, October 13, 2011 Time: Networking: 6:00-7:00 PM, Seminar: 7:00-8:45 PM Location: SFSU Campus, Science (SCI) - Room 201, 1600 Holloway Ave (off 19th Avenue), San Francisco SFSU directions and parking: http://www.sfsu.edu/~... - we recommend parking structure 20, then follow the map http://www.sfsu.edu/~... Cost: No charge (light refreshments will be served) Agenda Welcome - Dragutin Petkovic, Director, CCLS Overview of Seminar – Bob Dunkle Experiences and perspective in data mining and predictive analytics – Panelists - How is disparate data aggregated to include relevant data sets for mining applications? - Methods and processes to characterize the data and optimize predictive analytics performance - Methods validation - Use cases Panel conversation and open Q&A Sponsors CCLS - SFSU Center for Computing for Life Sciences http://cs.sfsu.edu/cc... IBM Almaden Research Center http://www.almaden.ib...

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  • Mike

    will there be a video posted online after the event?

    October 12, 2011

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