Functions and Phenotypes by Integrative Network Analysis Abstract: The rapid accumulation of genomics data provides unprecedented opportunities to systematically infer gene functions, regulatory networks, and phenotype associations. In this talk, we develop several graph-based data mining algorithms to integrate diverse genomics data, especially the vast amount of microarray data in the public repositories. A series of microarray data sets are modeled as a series of co-expression networks, in which we search for frequently occurring network patterns. Our integrative approach for functional annotation provides three major advantages over the commonly used microarray analysis methods: (1) enhance signal to noise separation (2) identify functionally related genes without co-expression, and (3) provides a way to predict gene functions in a context-specific way. Furthermore, we show that frequently occurring co-expression clusters are more likely to represent transcriptional modules than those clusters derived from a single microarray dataset. In addition, we propose the concept of "second-order correlation" which enables us to trace the upstream events of transcription cascades. Finally, we develop methods to systematically identify phenotype specific network patterns and regulatory modules.