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DATA MINING OF POST GENOME-WID

This dissertation, "Data Mining of Post Genome-wide Association Studies and Next Generation Sequencing" by 桂宏胜, Hongsheng, Gui, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Genome-wide association studies (GWAS) have been successfully applied to several complex diseases, yielding many confirmed associations. Nonetheless, at most they have explained half the genetic variance and often much less. It is quite apparent the rich GWAS datasets contain far more information than is typically uncovered using the most common univariate analysis approaches. The focus of the present thesis is on methods to extract the most information from GWAS and on post-GWAS experimental strategies, divided in four broad approaches. The first approach involves use of candidate gene studies to explore epistasis and gene by environment interactions, using samples of two different disorders, Hirschsprung disease (HSCR) and cognitive decline. For HSCR, previous studies identified rare and common variants in two genes, RET and NRG1, to be predisposing to disease, and further demonstrated a statistical interaction between common variants in these two genes. In this thesis, joint effects between common and rare variants both within and across the two genes were demonstrated by statistical modelling and then supported by functional interaction. For cognitive decline, SNPs previously implicated in Alzheimer's disease were examined for epistasis and gene-environment interaction in an independent sample of elderly Chinese. The ACE rs1800764_C heterozygote in combination with below-college educational level was found to result in greater cognitive decline. These two studies demonstrate the utility of post-GWAS candidate gene studies in detectinginteraction effects. The next two approaches were adopted on GWAS summary statistics at the SNP level. One of them involves meta-analysis applied to 11 epilepsy GWAS datasets, to increase power and explore whether findings are population-specific or general across populations. Two novel susceptibility genes (SCN1a and PCDH7) were identified using this approach. Furthermore, the previously identified epilepsy risk variant CAMSAP1L1 was found to only be a risk factor for Chinese focal epilepsy patients. The other summary statistic approach involved the development of a revised GWAS pathway analysis pipeline to search for effective genes or gene-sets. Its application to two autoimmune diseases revealed that multiple pathways might be dysfunctional simultaneously and hence contribute jointly to disease status. In addition, it indicated the pipeline was powerful for mining moderate/small genetic effects on common disorders. The last approach to post-GWAS analysis involves the use of next-generation sequencing (NGS). To this end, an automated NGS pipeline for variant calling, filtering and prioritization was established, specifically designed for gene burden analysis, recurrent gene sharing and de novo mutation (DNM) identification. The pipeline was applied to NGS sequencing of 62 candidate genes and also whole exomes of HSCR patients and their parents. Results indicated that multiple rare damaging inherited variants in several genes contribute to HSCR; in addition, loss of function DNMs were significantly enriched in HSCR probands. This thesis demonstrates the utility of data mining approaches for the dissection and exploration of genetic determinants of complex diseases. Such methods and their results should ultimately contribute to genetic diagnosis and improving treatment for complex disorders. Subjects: Data

This dissertation, "Data Mining of Post Genome-wide Association Studies and Next Generation Sequencing" by 桂宏胜, Hongsheng, Gui, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative ...

Using Data Mining Techniques to Discover Customer Behavioral Patterns for Direct Marketing in Mobile Telecommunication Industry

This dissertation, "Using Data Mining Techniques to Discover Customer Behavioral Patterns for Direct Marketing in Mobile Telecommunication Industry" by Xi, Chen, 陳熹, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b4098794 Subjects: Cellular telephone equipment industry - Marketing Data mining

This dissertation, "Using Data Mining Techniques to Discover Customer Behavioral Patterns for Direct Marketing in Mobile Telecommunication Industry" by Xi, Chen, 陳熹, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) ...

Data-Mining as a Methodology for Explaining Written Narratives

An Application on Understanding the Breast Cancer Experience Among Hong Kong Chinese Women

This dissertation, "Data-mining as a Methodology for Explaining Written Narratives: an Application on Understanding the Breast Cancer Experience Among Hong Kong Chinese Women" by Wai, Fu, 符瑋, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Data-mining as a methodology for explaining written narratives: an application on understanding the breast cancer experience among Hong Kong Chinese women. Submitted by Fu Wai For the degree of Doctor of Philosophy at the University of Hong Kong July 2007 The relationship between features in self-initiative written narratives and various mental health markers was investigated. 200 self-initiated writings on breast cancer experience by Chinese patients were collected. The narrative features in these written narratives were analyzed along side with participants' scores in various pre-intervention mental health markers and post-intervention one year follow up mental health markers. Clinical data mining procedure with subsequent investigative phenomenological analysis were adopted in this study. Six sections in cancer narratives, namely context, premonition, diagnosis, treatment, current status, and comment/ reflection, were identified. The word count in comment/reflection section is associated with post-traumatic growth. Negative end-tone of the narrative implies higher score in GHQ, Perceived Stress Scale, Hospital Anxiety and Depression Scale, and negative aspects in Chinese Coherence of Cancer Scale and Mini-MAC (Chinese version). Negative comments on people are closely linked to end-tone of the whole narrative, and anger is found to be the most detrimental to one's mental health. Unlike the case in Caucasian counterparts, Hong Kong Chinese subjects seldom write about sexuality issues in their cancer narratives. Those who believed that cancer is having a metaphysical root were having a larger support network indicated by Yale Social Support scale. The findings in this study provide lights on adopting self-initiated written cancer narratives as preliminary screening tools, and the implications of hermeneutic circle in post-traumatic growth are discussed. Results are discussed in light of the cognitive process theory and disinhibition theory. (237 words) DOI: 10.5353/th_b3955791 Subjects: Breast - Cancer - Personal narratives Breast - Cancer - Patients - Mental health - China - Hong Kong Data mining

This dissertation, "Data-mining as a Methodology for Explaining Written Narratives: an Application on Understanding the Breast Cancer Experience Among Hong Kong Chinese Women" by Wai, Fu, 符瑋, was obtained from The University of Hong ...

Data Mining Algorithms for Genomic Analysis

This dissertation, "Data Mining Algorithms for Genomic Analysis" by Sio-iong, Ao, 區小勇, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled DATA MINING ALGORITHMS FOR GENOMIC ANALYSIS Submitted by Ao, Sio Iong for the degree of Doctor of Philosophy at The University of Hong Kong in January 2007 With the results of many different genome-sequencing projects, hundreds of genomes from all branches of species have become available. Currently, one important task is to search for ways that can explain the organization and function of each genome. Data mining algorithms become very useful to extract the patterns from the data and to present it in such a way that can better our understanding of the structure, relation, and function of the subjects. In this work, data mining algorithms have been developed for solving some frontier problems in genomic analysis. It is estimated that there exist about ten million single-nucleotide polymorphisms (SNPs) in the human genome. The complete screening of all the SNPs in a genomic region becomes an expensive undertaking. The problem of selecting a subset of informative SNPs (tag SNPs) has been formulated as a hierarchical clustering problem with the development of a suitable similarity function for measuring the distances between the clusters. The proposed algorithm takes account of both functional and linkage disequilibrium information with the asymmetry thresholds for different SNPs, and does not have the difficulties of the block-detecting methods, which can result in different block boundaries. Experimental results supported that the algorithm is cost-effective for tag-SNP selection. More compact clusters can be produced with the algorithm to improve the efficiency of association studies. There are several different advantages of the linkage disequilibrium maps (LD maps) for genomic analysis. In this study, the construction of the LD mapping has been formulated as a non-parametric constrained unidimensional scaling problem, which is based on the LD information among the SNPs. This is different from the previous LD map, which is derived from the given Malecot model. Two procedures, one with the formulation as the least squares problem with nonnegativity and the other with the iterative algorithms, have been considered to solve this problem. The proposed maps can accommodate recombination events that have accumulated. Application of the proposed LD maps for human genome is presented. The linkage disequilibrium patterns in the LD maps can provide the genomic information like the hot and cold recombination regions, and can facilitate the study of recent selective sweeps across the human genome. Microarray has been the most widely used tool for assessing differences in mRNA abundance in the biological samples. Previous studies have successfully employed principal components analysis-neural network as a classifier of gene types, with continuous inputs and discrete outputs. An algorithm has been developed for testing the predictability of gene expression time series with PCA and NN components on a continuous numerical inputs and outputs basis. Comparisons of results support that our approach is a more realistic model for the gene network from a continuous prospective. DOI: 10.5353/th_b3831982 Subjects: Data mining Algorithms Genomics - Data processing

In this work, data mining algorithms have been developed for solving some frontier problems in genomic analysis. It is estimated that there exist about ten million single-nucleotide polymorphisms (SNPs) in the human genome.

Radio Polarisation Study of the Snail Pulsar Wind Nebula in Supernova Remnant G327.1-1.1

This dissertation, "Radio Polarisation Study of the Snail Pulsar Wind Nebula in Supernova Remnant G327.1-1.1" by Yik-ki, Ma, 馬奕騏, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Pulsar wind nebulae (PWNe) are believed to be acceleration sites of cosmic rays in the Galaxy. In the acceleration process, magnetic field plays an important role. Radio polarisation measurements offer a direct probe of the magnetic field geometry of astronomical objects, but such experiments have rarely been conducted on PWNe, especially for evolved systems. PWNe can be crushed by the supernova reverse shock at an age of 10 kyr. Previous hydrodynamical simulations show that such interactions can result in a turbulent environment in the nebula interior, suggesting a tangled magnetic field. In this thesis, I present a radio study of the Snail PWN in the composite supernova remnant G327.1-1.1 using the Australia Telescope Compact Array. This PWN is believed to have already interacted with the supernova reverse shock. The study reveals a highly ordered magnetic field configuration in the Snail, which can be explained if either the shockwave could not penetrate into the PWN interior to drive the turbulence, or the characteristic turbulence scale is large. A toy model is built to estimate the turbulence scale assuming the latter scenario. It is found that a simulated PWN with a turbulence scale of one-eighth to one-sixth of the nebula radius and a pulsar wind filling factor of 50-75% can match the observation results. This suggests significant mixing between supernova ejecta and pulsar wind material in this system. In addition, the Snail exhibits a subsonic comet-like protrusion extending from the putative neutron star. Cometary PWNe were found to exhibit a variety of magnetic field configurations but the exact reason remains unclear. The polarisation observations of this system revealed a magnetic field parallel to the nebula elongation, similar to what was found in the Mouse (G359.23-0.82) and the handle of the Frying Pan (G315.78-0.23). This adds an important sample to cometary PWNe for future MHD modelling. Subjects: Supernova remnants Pulsars

This dissertation, "Radio Polarisation Study of the Snail Pulsar Wind Nebula in Supernova Remnant G327.1-1.1" by Yik-ki, Ma, 馬奕騏, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to ...