This PhD thesis presents the work carried out at Center for Biological Sequence Analysis, Technical University of Denmark. The projects presented in this thesis are a purely bioinformatic in nature. Included in this thesis are the two projects that focus on the gene regulatory events mediated by miRNAs in the context of cancer biology, drug resistance and disease progression. The first project described in Chapter 6 addresses the problem of tamoxifen resistance, an anti-estrogen drug that is generally highly effective in the treatment of ER-positive breast cancers. The underlying molecular mechanisms for the acquired resistance to tamoxifen are not very well understood. Therefore, with the aid of miRNA and gene expression profiles for MCF7/S0.5 (tamoxifen sensitive) and three MCF7/S0.5 derived tamoxifen resistant cell lines, we obtained several miRNA-mediated regulatory events in the tamoxifen resistant cell lines. Following a systems biology approach of integrating evidences of functional interactions such as transcription factor (TF)-miRNA interactions, we have identified a number of biologically relevant pathways involved in the development of tamoxifen resistance. Chapter 7 presents a study highlighting the role of miRNAs in the transformation of ocular mucosa associated lymphoid tissue lymphoma (MALT) to the high-grade diffuse large B-cell lymphoma (DLBCL) of eye. Several tumor suppressive miRNAs were found to be dysregulated in DLBCL, suggesting their possible role in disease transformation. Many of those were under transcriptional regulation by MYC and NFKB1, the key transcription factors involved in lymphomas. Furthermore, upstream regulators of NFKB1 were also repressed, suggesting a possible loss of regulation of NFKB1 may contribute to the activation of NF-_B signaling pathway, and thereby to the disease transformation. In summary, this thesis focuses on regulatory role of miRNAs in drug resistance and disease progression. The findings provide hints toward various biologically and perhaps therapeutically relevant gene regulatory events. This thesis demonstrates the right choice of data analysis techniques combined with a systems biology approach provides better understanding of the complex biology.