Data Mining and Predictive Analytics Seminar Part 2 - Research Applications

San Francisco, CA, September 11, 2011 --(PR.com)-- Please register at: http://sfsudmparesearch.eventbrite.com

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, a follow-on to the prior seminar 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.

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 101, 1600 Holloway Ave (off
19th Avenue), San Francisco

SFSU directions and parking: http://www.sfsu.edu/~parking/ - we recommend
parking structure 20, then follow the map

http://www.sfsu.edu/~sfsumap/southeast.htm

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/ccls/index.html

IBM Almaden Research Center http://www.almaden.ibm.com/

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