Microarray expression data which contains the expression levels of a large number of simultaneously observed genes have been used in many scientific research and clinical studies. Due to its high dimensionalities, selecting a small number of genes has shown to be beneficial for many tasks such as building prediction models from the microarray expression data or gene regulatory network discovery. Traditional gene selection methods, however, fail to take the class distribution into the selection process. In biomedical science, it is very common to have microarray expression data which is severely biased with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). These sample sets with biased distributions require special attention from researchers for identification of genes responsible for a particular disease. In this thesis, we propose three filtering techniques, Higher Weight ReliefF, ReliefF with Differential Minority Repeat and ReliefF with Balanced Minority Repeat to identify genes responsible for fatal diseases from biased microarray expression data. Our solutions are evaluated on five well-known microarray datasets, Colon, Central Nervous System, DLBCL Tumor, Lymphoma and ECML Pancreas. Experimental comparisons with the traditional ReliefF filtering method demonstrate the effectiveness of the proposed methods in selecting informative genes from microarray expression data with biased sample distributions. / by Abu Hena Mustafa Kamal. / Thesis (M.S.C.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

Indiana University-Purdue University Indianapolis (IUPUI) / During pregnancy, maternal liver exhibits robust growth to meet the metabolic demands of the developing placenta and fetus. Although hepatocyte hypertrophy and hyperplasia are seen in the maternal liver, the molecular and cellular mechanisms mediating the maternal hepatic adaptations to pregnancy is poorly understood. Previous microarray analysis revealed a most upregulated gene named Ascl1, a transcription factor essential for neural development, in the maternal liver at mid-gestation. The aims of the study were to (1) validate the activation of Ascl1 gene; (2) identify Ascl1-expressing cells; and (3) determine the fate of Ascl1-expressing cells, in the maternal liver during the course of gestation. Timed pregnancy was setup in mice and the maternal livers were collected at various stages of gestation. Maternal hepatic Ascl1 mRNA expression was evaluated by qRT-PCR and northern blotting. The results demonstrated that the transcript level of maternal hepatic Ascl1 is exponentially increased during the second half of pregnancy in comparison with a non-pregnant state. Using a Ascl1-GFP mouse model generated by others to monitor the behavior of neural progenitor cells, we found that maternal hepatic Ascl1-expressing cells are non-parenchymal cells, very small in size, and expanding during pregnancy. To map the fate of this cell population, we generated an in vivo tracing mouse model named Ascl1-CreERT2/ROSA26-LacZ. Using this model, we permanently labeled maternal hepatic Ascl1-expressing cells at midgestation by giving tamoxifen and analyzed the labeled cells in the maternal liver prior to parturition. We observed that the initial small Ascl1-expressing cells undergoing expansion at mid-gestation eventually became hepatocyte-like cells at the end stage of pregnancy. Taken together, our findings strongly suggest that Ascl1-expressing cells represent a novel population of hepatic progenitor cells and they can differentiate along hepatocyte lineage and contribute to pregnancy-induced maternal liver growth. Further studies are needed to firmly establish the nature and property of maternal hepatic Ascl1-expressing cells. At this stage, we have gained significant insights into the cellular mechanism by which the maternal liver adapts to pregnancy.

Indiana University-Purdue University Indianapolis (IUPUI) / Break-induced replication (BIR) is a mechanism to repair double-strand breaks (DSBs) that possess only a single end that can find homology in the genome. This situation can result from the collapse of replication forks or telomere erosion. BIR frequently produces various genetic instabilities including mutations, loss of heterozygosity, deletions, duplications, and template switching that can result in copy-number variations (CNVs). An important type of genomic rearrangement specifically linked to BIR is half crossovers (HCs), which result from fusions between parts of recombining chromosomes. Because HC formation produces a fused molecule as well as a broken chromosome fragment, these events could be highly destabilizing. Here I demonstrate that HC formation results from the interruption of BIR caused by a defective replisome or premature onset of mitosis. Additionally, I document the existence of half crossover instability cascades (HCC) that resemble cycles of non-reciprocal translocations (NRTs) previously described in human tumors. I postulate that HCs represent a potent source of genetic destabilization with significant consequences that mimic those observed in human diseases, including cancer.