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ItemEffective integration of diverse biological datasets for better understanding of cancerGaire, Raj Kumar ( 2012)Cancer is a disease of malfunctioning cells. Nowadays, experiments in cancer research have been producing a large number of datasets that contain measurements of various aspects of cancer. Similarly, datasets in cellular biology are becoming better organised and increasingly available. An effective integration of these datasets to understand the mechanisms of cancers is a challenging task. In this research, we develop novel integration methods and apply them to some diverse datasets of cancer. Our analysis finds that subtypes of cancers share common features that may be useful to direct cancer biologists to find better cure of cancers. As our first contribution, we developed MIRAGAA, a statistical approach to assess the coordinated changes of genome copy numbers and microRNA (miRNA) expression. Genetic diseases like cancer evolve through microevolution where random lesions that provide the biggest advantage to the diseases can stand out in their frequent occurrence in multiple samples. At the same time, a gene function can be changed by aberration of the corresponding gene or modification of expression levels of microRNA which attenuates the gene. In a large number of disease samples, these two mechanisms might be distributed in a coordinated and almost mutually exclusive manner. Understanding this coordination may assist in identifying changes which significantly produce the same functional impact on cancer phenotype, and further identify genes that are universally required for cancer. MIRAGAA has been evaluated on the cancer genome atlas (TCGA) Glioblastoma Multiforme datasets. In these datasets, a number of genome regions coordinating with different miRNAs are identified. Although well known for their biological significance, these genes and miRNAs would be left undetected for being not significant enough if the two datasets were analysed individually. Genes can show significant changes in their expression levels when genetically diseased cells are compared with non-diseased cells. Biological networks are often used to analyse the genetic expression profiles to identify active subnetworks (ASNs) in the diseases. Existing methodologies for discovering ASNs mostly use node centric approaches and undirected PPI networks. This can limit their ability to find the most meaningful ASNs. As our second contribution, we developed Bionet which aims to identify better ASNs by using (i) integrated regulatory networks, (ii) directions of regulations of genes, and (iii) combined node and edge scores. We simplify and extend previous methodologies to incorporate edge evaluations and lessen their sensitivity to significance thresholds. We formulate our objective functions using mixed integer linear programming (MIP) and show that optimal solutions may be obtained. As our third contribution, we integrated and analysed the disease datasets of glioma, glioblastoma and breast cancer with pathways and biological networks. Our analysis of two independent breast cancer datasets finds that the basal subtype of this cancer contains positive feedback loops across 7 genes, AR, ESR1, MYC, E2F2, PGR, BCL2 and CCND1 that could potentially explain the aggressive nature of this cancer subtype. A comparison of the basal subtype of breast cancer and the mesenchymal subtype of glioblastoma ASNs shows that an ASN in the vicinity of IL6 is conserved across the two subtypes. CD44 is found to be the most outcome predictor gene in both glioblastoma and breast cancer and may be used as biomarker. Our analysis suggests that cancer subtypes from two different cancers can show molecular similarities that are identifiable by using integrated biological networks.