MicroRNAs (miRNAs) are small non-coding RNAs that are responsible for post-transcriptional gene silencing.These miRNAs are associated with the RISC (RNA-Induced Silencing Complex) that uses a seed sequence to target specific genes. Both expression of miRNAs and genes within virus-infected cells provide independent data in identifying biological hypotheses concerning regulation changes during infection. To do this, three data types are available for integration: gene expression microarray, miRNA expression microarray and RISC-Immunoprecipitation (RISC-IP) microarray. RISC-IP data provides a look at what genes are associated with the complex during infection versus uninfected cells (mock). Incorporating three independent data types allows a more complete representation of the host response to Flavivirus infection which is key to identifying miRNA regulators. This thesis integrates all three types of data for Flavivirus-infected cells using the statistical programming environment R to identify statistically enriched miRNAs regulating host response.