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Previous studies on IT investment using event studies investigated different aspects of IT. For example, Dos Santos et al. (1993) studied IT innovativeness andChatterjee et al. (2002) focused on different functionality aspects of IT (e.g., infrastructure and application). Apart from general IT investment, there were also a few studies specific to IT applications (e.g., ERP, e-business, and security). Nevertheless, the steps of the event study adopted in previous studies were similar. All previous studies first analyzed abnormal return based on subsamples followed by subsampling analysis. Most studies adopted a three-day event window around IT investment announcements. Both parametric and nonparametric tests (e.g., sign test and Corrado's rank test) were used to determine the level of significance of abnormal return in the estimation period. Furthermore, most of the sample data were from the United States. Meng and Lee (2007) was the only study that compared the reaction of IT investment in the United States with that in China. A summary of previous research on IT investment using event studies is shown in Table A1.

Summary: Correlating disease mutations with clinical and phenotypic information such as drug response or patient survival is an important goal of personalized cancer genomics and a first step in biomarker discovery. HyperModules is a network search algorithm that finds frequently mutated gene modules with significant clinical or phenotypic signatures from biomolecular interaction networks.Availability and implementation: HyperModules is available in Cytoscape App Store and as a command line tool at www.baderlab.org/Sofware/HyperModules.Contact: Juri.Reimand@utoronto.ca or Gary.Bader@utoronto.caSupplementary information: Supplementary data are available at Bioinformatics online

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