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A hybrid GA (genetic algorithm)-based clustering (HGACLUS) schema, combining merits of the Simulated Annealing, was described for finding an optimal or near-optimal set of medoids. This schema maximized the clustering success by achieving internal cluster cohesion and external cluster isolation. The performance of HGACLUS and other methods was compared by using simulated data and open microarray gene-expression datasets. HGACLUS was generally found to be more accurate and robust than other methods discussed in this paper by the exact validation strategy and the explicit cluster number.

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Test case prioritization for regression testing is an approach that schedules test cases to improve the efficiency of service-oriented workflow application testing. Most of existing prioritization approaches range test cases according to various metrics (e.g., statement coverage, path coverage) in different application context. Service-oriented workflow applications orchestrate web services to provide value-added service and typically are long-running and timeconsuming processes. Therefore, these applications need more precise prioritization to execute earlier those test cases that may detect failures. Surprisingly, most of current regression test case prioritization researches neglect to use internal structure information of software, which is a significant factor influencing the prioritization of test cases. Considering the internal structure information and fault propagation behavior of modifications respect to modified version for service-oriented workflow applications, we present in this paper a new regression test case prioritization approach. Our prioritization approach schedules test cases based on dependence analysis of internal activities in serviceoriented workflow applications. Experimental results show that test case prioritization using our approach is more effective than conventional coverage-based techniques.

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Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV (gene by variety) interaction using the adjusted unbiased prediction (AUP) method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.