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Methods of Microarray Data Analysis II
High-throughput genomic analysis provides insight into a complicated biological phenomena. However, the vast amount of data produced from upto-date biological experimental processes needs appropriate data mining techniques to extract useful information. In this paper, we propose a method based on cluster analysis and Bayesian network learning for the molecular pharmacology of cancer. Specifically, the NCI60 dataset is analysed by soft topographic vector quantization (STVQ) for cluster analysisdoi:10.1007/0-306-47598-7_12 fatcat:emzwlihmmzfgxihjhkdqn5uqlu