Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Scalable Techniques for Mining Causal Structures
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Screening and interpreting multi-item associations based on log-linear modeling
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Graphical modeling based gene interaction analysis for microarray data
ACM SIGKDD Explorations Newsletter
Exploring gene causal interactions using an enhanced constraint-based method
Pattern Recognition
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The prediction and identification of physical and genetic interactions from gene expression data is one of the most challenging tasks of modern functional genomics. Although various interaction analysis methods have been well studied in data mining and statistics fields, we face new challenges in applying these methods to the analysis of microarray data. In this paper, we investigate an enhanced constraint based approach for causal structure learning. We integrate with graphical gaussian modeling and use its independence graph as input of next phase's causal analysis. We also present graphical decomposition techniques to further improve the performance. The experimental results show that our enhanced method makes it feasible to explore causal interactions interactively for applications with a large number of variables (e.g., microarray data analysis).