A simple yet effective data integration approach to tree-based microarray data classification

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 experimental devices and environments. Although microarray data is well known for its inconsistencies across labs, we demonstrate that it is possible to build consistent models using data sets from multiple labs. We report our method, experimental results and observations in the paper.