Integrated gene expression analysis of multiple microarray data sets based on a normalization technique and on adaptive connectionist model

Research with microarray gene expression analysis has primarily been on expression profiling based on one set of microarray data. This paper presents a novel approach to integrated analysis and modeling of microarray data from multiple sources. Normalization method is applied to different data sets before they are used together in an adaptive connectionist classification system. The method is demonstrated on a bench-mark case study problem of classifying Diffuse Large B-cell lymphoma (DLBCL) and Follicular lymphoma (FL). For the purpose of comparison, different normalization techniques were applied and connectionist models were created from one or more microarray data sets and then tested on the others. The results show that with the use of proper normalization and modeling techniques, a model based on one set of data can be used to classify microarray data from totally different sources. For the modeling part, evolving connectionist systems (ECOS) are used that allow for new data to be added in an incremental way so that connectionist systems can be built for on-line adaptive learning where new data from various sources can be added into the system.