title: A simple yet effective data integration approach to tree-based microarray data classification.
abstract: 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.
authors.affiliation: School of Computer and Information Science, University of South Australia, Mawson Lakes, 5095, Australia. liu.liu@unisa.edu.au
authors.forename: Lin
authors.initials: L
authors.lastname: Liu
authors.forename: Yi
authors.initials: Y
authors.lastname: Li
authors.forename: Bing
authors.initials: B
authors.lastname: Liu
authors.forename: Jiuyong
authors.initials: J
authors.lastname: Li
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008175:Lung Neoplasms / Q000378:metabolism*; D009363:Neoplasm Proteins / Q000032:analysis*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D018511:Systems Integration
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins