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Different biological labs conduct similar experiments on same diseases. It is highly desirable to have a better model based on more experimental results than that on a single result. In this paper, we propose a method for integrating microarray data from multiple sources for building classification models. To test the method, we use three real world microarray data sets from different labs with different...
Data mining techniques have been extensively applied in bioinformatics to analyze biomedical data. In this paper, we choose the Rapid-I??s RapidMiner as our tool to analyze a Pima Indians Diabetes Data Set, which collects the information of patients with and without developing diabetes. The discussion follows the data mining process. The focus will be on the data preprocessing, including attribute...
Synthetic data sets can be useful in a variety of situations, including repeatable regression testing and providing realistic - but not real - data to third parties for testing new software. Researchers, engineers, and software developers can test against a safe data set without affecting or even accessing the original data, insulating them from privacy and security concerns as well as letting them...
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