Thesis (Ph. D.)--University of New South Wales, 2003. / Also available online.

Thesis (Ph.D.) - University of Glasgow, 2007. / Ph.D. thesis submitted to the Division of Virology, Institute of Biomedical and Life Sciences, University of Glasgow, 2007. Includes bibliographical references. Print version also available.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (Ph.D.)--Drexel University, 2010. / Includes abstract and vita. Includes bibliographical references (leaves 94-108).

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Classification of tissues and diseases based on gene expression data is a powerful application of DNA microarrays. Many popular classifiers like support vector machines, nearest-neighbour methods, and boosting have been applied successfully to this problem. However, it is difficult to determine from these classifiers which genes are responsible for the distinctions between the diseases. We propose a novel framework for classification of gene expression data based on notion of condition-specific clusters of co-expressed genes called xMotifs. Our xMotif-based classifier is biologically interpretable: we show how we can detect relationships between xMotifs and gene functional annotations. Our classifier achieves high-accuracy on leave-one-out cross-validation on both two-class and multi-class data. Our technique has the potential to be the method of choice for researchers interested in disease and tissue classification. / Master of Science

Streptococcus sanguinis is a gram-positive, non-motile bacterium native to human mouths. It is the primary cause of endocarditis and is also responsible for tooth decay. Two-component systems (TCSs) are commonly found in bacteria. In response to environmental signals, TCSs may regulate the expression of virulence factor genes. Gene co-expression networks are exploratory tools used to analyze system-level gene functionality. A gene co-expression network consists of gene expression profiles represented as nodes and gene connections, which occur if two genes are significantly co-expressed. An adjacency function transforms the similarity matrix containing co-expression similarities into the adjacency matrix containing connection strengths. Gene modules were determined from the connection strengths, and various network connectivity measures were calculated. S. sanguinis gene expression profile data was loaded for 2272 genes and 14 samples with 3 replicates each. The soft thresholding power β=6 was chosen to maximize R2 while maintaining a high mean number of connections. Nine modules were found. Possible meta-modules were found to be: Module 1: Blue & Green, Module 2: Pink, Module 3: Yellow, Brown & Red, Module 4: Black, Module 5: Magenta & Turquoise. The absolute value of module membership was found to be highly positively correlated with intramodular connectivity. Each of the nine modules were examined. Two methods (intramodular connectivity and TOM-based connectivity followed by network mapping) for identifying candidate hub genes were performed. Most modules provided similar results between the two methods. Similar rankings between the two methods can be considered equivalent and both can be used to detect candidate hub genes. Gene ontology information was unavailable to help select a module of interest. This network analysis would help researchers create new research hypotheses and design experiments for validation of candidate hub genes in biologically important modules.

This research is concerned with analyzing a set of viral genomes to elucidate the underlying characteristics and determine the information-theoretic aspects of the genomic signatures. The goal of this study thereof, is tailored to address the following: (i) Reviewing various methods available to deduce the features and characteristics of genomic sequences of organisms in general, and particularly focusing on the genomes pertinent to viruses; (ii) applying the concepts of information-theoretics (entropy principles) to analyze genomic sequences; (iii) envisaging various aspects of biothermodynamic energetics so as to determine the framework and architecture that decide the stability and patterns of the subsequences in a genome; (iv) evaluating the genomic details using spectral-domain techniques; (v) studying fuzzy considerations to ascertain the overlapping details in genomic sequences; (vi) determining the common subsequences among various strains of a virus by logistically regressing the data obtained via entropic, energetics and spectral-domain exercises; (vii) differentiating informational profiles of coding and non-coding regions in a DNA sequence to locate aberrant (cryptic) attributes evolved as a result of mutational changes and (viii) finding the signatures of CDS of genomes of viral strains toward rationally conceiving plausible designs of vaccines. Commensurate with the topics indicated above, necessary simulations are proposed and computational exercises are performed (with MatLabTM R2009b and other software as needed). Extensive data gathered from open-literature are used thereof and, simulation results are verified. Lastly, results are discussed, inferences are made and open-questions are identified for future research. / by Sharmistha P. Chatterjee. / Thesis (Ph.D.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

DNA microarray technology is the most notable high throughput technology which emerged for functional genomics in recent years. Patterns in microarray data provide clues of gene functions, cell types, and interactions among genes or gene products. Since the scale of microarray data keeps on growing, there is an urgent need for the development of methods and tools for the analysis of these huge amounts of complex data. / Interesting patterns in microarray data can be patterns appearing with significant frequencies or patterns appearing special trends. Firstly, an algorithm to find biclusters with coherent values is proposed. For these biclusters the subset of genes (or samples) show some similarities, such as low Euclidean distance or high Pearson correlation coefficient. We propose Average Correlation Value (ACV) to measure the homogeneity of a bicluster. ACV outperforms other alternatives for being applicable for biclusters of more types. Our algorithm applies dominant set approach to create sets of sorting vectors for rows of the data matrix. In this way, the co-expressed rows of the data matrix could be gathered. By alternatively sorting and transposing the data matrix the blocks of co-expressed subset are gathered. Weighted correlation coefficient is used to measure the similarity in the gene level and the sample level. Their weights are updated each time using the sorting vector of the previous iteration. Genes/samples which are assigned higher weights contribute more to the similarity measure when they are used as features for the other dimension. Unlike the two-way clustering or divide and conquer algorithm, our approach does not break the structure of the whole data and can find multiple overlapping biclusters. Also the method has low computation cost comparing to the exhaustive enumeration and distribution parameter identification methods. / Next, algorithms to find biclusters with coherent evolutions, more specific, the order preserving patterns, are proposed. In an Order Preserving Cluster (OP-Cluster) genes induce the same relative order on samples, while the exact magnitude of the data are not regarded. By converting each gene expression vector into an ordered label sequence, we transfer the problem into finding frequent orders appearing in the sequence set. Two heuristic algorithms, Growing Prefix and Suffix (GPS) and Growing Frequent Position (GFP) are presented. The results show these methods both have good scale-up properties. They output larger OP-Clusters more efficiently and have lower space and computation space cost comparing to the existing methods. / We propose the idea of Discovering Distinct Patterns (DDP) in gene expression data. The distinct patterns correspond to genes with significantly different patterns. DDP is useful to scale-down the analysis when there is little prior knowledge. A DDP algorithm is proposed by iteratively picking out pairs of genes with the largest dissimilarities. Experiments are implemented on both synthetic data sets and real microarray data. The results show the effectiveness and efficiency in finding functional significant genes. The usefulness of genes with distinct patterns for constructing simplified gene regulatory network is further discussed. / Teng, Li. / Adviser: Laiwan Chan. / Source: Dissertation Abstracts International, Volume: 71-01, Section: B, page: 0446. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 118-128). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese.

Biocompatible polymers are used exhaustively within the biomedical arena, demonstrating a mechanical and chemical diversity that few other materials possess. As polymer technologies evolves to cater for new medical demands, even the most niche biomedical application becomes an achievable reality. However, the discovery of new polymers is hindered by the complexity and intricacy in which the biological milieu interacts with a new substrate, reducing the ability to predict the appropriateness of a certain polymer for a specific application. This drawback can be countered by the high-throughput evaluation of large numbers of chemically diverse polymer candidates. In this thesis, the use of polymer microarrays is invoked to address two separate medically-relevant issues: the control of inflammation, and the improvement of cancer screening. In addition, I provide details of how polymer microarray techniques and technology can be employed to expand the repertoire of biomaterials research. Mitochondrial DNA (mtDNA) is an alarm molecule that contributes to the cytokine storm observed during severe tissue injury. An application where control of this systemic inflammation is achieved through scavenging of mtDNA by a polymer was proposed. Primary screening highlighted that 166 out of the 380 polymers evaluated bound to blood cells, making them unsuitable for a blood-based application. The remaining 214 blood-compatible polymers were cross-examined for mtDNA binding. Through polymer microarray and subsequent scale-up of promising candidates, a poly(methoxyethyl methacrylate-co-di(ethylamino)ethyl acrylate-co-methoxyethyl acrylate) was found to have a remarkable ability to scavenge mtDNA. Removal of cell-free mtDNA using this polymer is proposed to remove a key trigger of systemic inflammation. Cervical cancer screening includes the cytological evaluation of patient material for developed or developing abnormalities. An application was sought that would enrich for cancerous/pre-cancerous cells and improve upon current standards for detection. Four cancerous cervical cell lines (HeLa, CaSki, SiHa, and C33a) and four precancerous cell lines (W12E, W12G, W12GPX, and W12GPXY) were interrogated to identify polymers with consistent binding that may improve routine cytological evaluation. A short-list of 24 polymers was assembled, and cells from liquid based cytology samples from healthy patient were spiked with DiI-labelled cancerous/precancerous cells and the short-listed polymers were re-evaluated for preferential binding. An enrichment of abnormal cervical cells was observed with three polymers, which could form the foundation for improved screening resources. Inkjet printing can be a useful tool in developing patterned substrates, such as polymer microarrays. A piezoelectric drop-on-demand printer was used to explore the methods in which these can be fabricated. A wettability assay using picolitre volumes was developed and used to characterise O2 plasma treatment of glass slides. Additionally, the printing of a cell-binding polymer using this approach enabled the decoration of cells with precise spatial resolution.