published_or_final_version / Mathematics / Doctoral / Doctor of Philosophy

Biological systems are complex in that they comprise large number of interacting entities, and their dynamics follow mechanic regulations for movement and biological function organization. Established computational modeling deals with studying and manipulating biologically relevant systems as a powerful approach. Inner structure and behavior of complex biological systems can be analyzed and understood by computable biological networks. In this thesis, models and computation methods are proposed for biological networks. The study of Genetic Regulatory Networks (GRNs) is an important research topic in genomic research. Several promising techniques have been proposed for capturing the behavior of gene regulations in biological systems. One of the promising models for GRNs, Boolean Network (BN) has gained a lot of attention. However, little light has been shed on the analysis of internal connection between the dynamics of biological molecules and network systems. Inference and completion problems of a BN from a given set of singleton attractors are considered to be important in understanding the relationship between dynamics of biological molecules and network systems. Discrete dynamic systems model has been recently proposed to model time-course microarray measurements of genes, but delay effect may be modeled as a realistic factor in studying GRNs. A delay discrete dynamic systems model is developed to model GRNs. Inference and analysis of networks is one of the grand challenges in modern statistical biology. Machine learning method, in particular, Support Vector Machine (SVM), has been successfully applied in predictions of internal connections embedded in networks. Kernels in conjunction with SVM demonstrate strong ability in performing various tasks such as biomedical diagnosis, function prediction and motif extractions. In biomedical diagnosis, data sets are always high dimensional which provide a challenging research problem in machine learning area. Novel kernels using distance-metric that are not common in machine learning framework are proposed for possible tumor differentiation discrimination problem. Protein function prediction problem is a hot topic in bioinformatics. The K-spectrum Kernel is among the top popular models in description of protein sequences. Taking into consideration of positive-semi-definiteness in kernel construction, Eigen-matrix translation technique is introduced in novel kernel formulation to give better prediction result. In a further step, power of Eigen-matrix translation technique in feature selection is demonstrated through mathematical formulation. Due to structure complexity of carbohydrates, the study of carbohydrate sugar chains has lagged behind compared to that of DNA and proteins. A weighted q-gram kernel is constructed in classifying glycan structures with limitations in feature extractions. A biochemically-weighted tree kernel is then proposed to enhance the ability in both classification as well as motif extractions. Finally the problem of metabolite biomarker discovery is researched. Human diseases, in particular metabolic diseases, can be directly caused by the lack of essential metabolites. Identification of metabolite biomarkers has significant importance in the study of biochemical reaction and signaling networks. A promising computational approach is proposed to identify metabolic biomarkers through integrating biomedical data and disease-specific gene expression data. / published_or_final_version / Mathematics / Doctoral / Doctor of Philosophy

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Identifying single nucleotide polymorphisms (SNPs) that are involved in common and complex diseases, such as cancer, is a major challenge in current molecular epidemiology. Knowledge of such SNPs is expected to enable timely diagnosis, effective treatment, and, ultimately, prevention of human disease. However, the tremendous number of SNPs on the human genome, which is estimated at more than eleven million, poses challenges to obtain and analyze the information of all the SNPs. In this thesis we address the problem of selecting representative SNP markers for supporting effective disease-gene association studies. Our goal is to facilitate the genotyping and analysis procedure, associated with such studies, by providing effective prioritization methods for SNP markers based on both their allele information and functional significance. However, the problem of SNP selection has been proven to be NP-hard in general, and current selection methods impose certain restrictions and use heuristics for reducing the complexity of the problem. We thus aim to develop new heuristic algorithms and systems to advance the state-of-the-art, while relaxing the restrictions. To address this challenge, we formulate several SNP selection problems and present novel algorithms and a database system based on the two major SNP selection approaches: tag SNP selection and functional SNP selection. Furthermore, we describe an innovative approach to combine both tag SNP selection and functional SNP selection into one unified selection process. We demonstrate the improved performance of all the proposed methods using comparative studies. / Thesis (Ph.D, Computing) -- Queen's University, 2009-06-22 15:26:14.061

Thesis (Ph. D.)--George Mason University, 2007. / Title from PDF t.p. (viewed Jan. 22, 2008). Thesis director: Iosif I. Vaisman. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Bioinformatics. Vita: p. 140. Includes bibliographical references (p. 133-139). Also available in print.

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2008. / Title from title screen (site viewed Jan. 15, 2009). PDF text: xiv, 144 p. : col. ill. ; 1 Mb. UMI publication number: AAT 3319848. Includes bibliographical references. Also available in microfilm and microfiche formats.

Thesis (Ph. D.)--University of Hong Kong, 2008.

Thesis (M. Phil.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 61-65) Also available in print.

Thesis (M.S.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 77-79)

Thesis (Ph.D.)--Virginia Commonwealth University, 2009. / Prepared for: Dept. of Microbiology and Immunology. Title from title-page of electronic thesis. Includes bibliographical references.