5:30 Hui Yang, San Francisco State University, 5:50 Arno Puder, San Francisco State University, 6:10 Elizabeth Runquist, San Francisco State University
Dr. Yang: Data mining is a relatively new interdisciplinary field in Computer Science. One of its primary objectives is to discover hidden knowledge patterns in datasets drawn from a wide range of domains, such as Web-based information retrieval, retail marketing, drug design, and many scientific disciplines (e.g., physics and biology). Many Data Mining techniques have been developed and successfully applied to a variety of real-world problems. In this short talk, we will first give a brief introduction to Data Mining by identifying key techniques and applications. We will then go over several ongoing Data Mining projects at SFSU. Finally, we summarize this talk by suggesting different ways through which you can be part of the Data Mining team at SFSU.
Dr. Runquist: We have developed a program, Q-Anal which if a web application is developed will allow researchers in the scientific, medical and diagnostic communities to analyze their comparative quantitative polymerase chain reaction (qPCR) data. The program requires the input of two comma separated values (csv) files; the raw delta Rn csv files exported from the ABI instrument and a user defined csv description file. The proposed web application will allow users to import their raw delta Rn csv files and will provide a ?template? description csv file which the user must complete and then submit with each delta Rn csv file. Once both files are submitted, the application will be required to specify to the program how many points should be used for analysis. After analysis; the application will provide users with results; Q-Anal generates a csv file. This application will allow users to quickly test the program under a number of different experimental conditions so that strengths and weaknesses of the program can establish with the goal of developing an extremely powerful method to quickly analyze q-PCR data by minimizing error.