Dissertations / Theses: 'Bioinformatics – Research'

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Freyhult, Eva. "New techniques for analysing RNA structure /." Uppsala, 2004. http://www.math.uu.se/research/pub/Freyhult1.pdf.

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Lesser, Alice. "Hereditarily optimal realizations /." Uppsala, 2004. http://www.math.uu.se/research/pub/Lesser1.pdf.

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Guo, Ling. "Bioinformatics in maize genome research." [Ames, Iowa : Iowa State University], 2007.

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Kusnierczyk, Waclaw. "Augmenting Bioinformatics Research with Biomedical Ontologies." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-2001.

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The main objective of the reported study was to investigate how biomedical ontologies, logically structured representations of various aspects of the biomedical reality, can help researchers in analyzing experimental data. The dissertation reports two attempts to construct tools for the analysis of high-throughput experimental results using explicit domain knowledge representations. Furthermore, integrative efforts made by the community of Open Biomedical Ontologies (OBO), in which the author has participated, are reported, and a framework for consistently connecting the Gene Ontology (GO) with the Taxonomy of Species is proposed and discussed.

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Elekwachi, Chijioke Obioma. "Bioinformatics resources to support bioremediation research." Thesis, University of Nottingham, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.595818.

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Contamination of ecosystems by xenobiotic substances has led to significant negative impacts on the ecologies and on the health and economic livelihood of the human populations in affected environments. Bioremediation, particularly microbial bioremediation, has proven to be a safe, low-cost and environmentally friendly method for remediation of such areas. However, a lack of complete understanding of the metabolic, enzymatic and cellular processes involved has made it difficult to model and predict outcomes of field processes. The ability of researchers to make critical decisions capable of influencing the direction and outcomes of these processes is also hampered. This study outlines the results of a survey and describes the electronic Microbial BioRemediation (eMBR) web portal, designed to improve collaboration in the bioremediation research community. It describes the structure, algorithms and output of three bioinformatics resources developed and deployed via the portal. eMBRLitMine addresses the problem of identifying which microorganisms would be suitable for remediating sites contaminated by named compounds. It combines named-entity recognition algorithms, a mySQL database, graph rendering technologies and Perl scripts to create, from the vast information available within published literature, a statistical co-occurrence matrix which it uses to infer possible associations between microorganisms and particular contaminants. This provides valuable insights into possible bacteria/contaminant relationships and highlights bacterial species that could be used in remediation of specified contaminants. eMBRCatalogue is a moderated and searchable database cataloguing bioremediation case studies. Implemented as an eXtensible Markup Language (XML) database employing a user-generated-content framework, it provides background knowledge necessary for planning and execution of bioremediation activities. Developed following the construction of a comprehensive metabolic biodegradation network, eMBRHelper enables the delineation of possible biodegradation pathways for named contaminants. By integrating relevant chemical, enzymatic and genomics information, it attempts to model the interplay between contaminants, enzymes, microorganisms and degradation pathway, enabling researchers to make informed decisions for improved outcomes, particularly for remediation exercises involving bioaugmentation. The study also analysed usage of the portal and resources, made recommendations for future developments and highlights avenues for further informatics support for the bioremediation research sector.

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Niklasson, Markus. "Coding to cure : NMR and thermodynamic software applied to congenital heart disease research." Doctoral thesis, Linköpings universitet, Kemi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-142785.

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Regardless of scientific field computers have become pivotal tools for data analysis and the field of structural biology is not an exception. Here, computers are the main tools used for tasks including structural calculations of proteins, spectral analysis of nuclear magnetic resonance (NMR) spectroscopy data and fitting mathematical models to data. As results reported in papers heavily rely on software and scripts it is of key importance that the employed computational methods are robust and yield reliable results. However, as many scientific fields are niched and possess a small potential user base the task to develop necessary software often falls on researchers themselves. This can cause divergence when comparing data analyzed by different measures or by using subpar methods. Therein lies the importance of development of accurate computational methods that can be employed by the scientific community. The main theme of this thesis is software development applied to structural biology, with the purpose to aid research in this scientific field by speeding up the process of data analysis as well as to ensure that acquired data is properly analyzed. Among the original results of this thesis are three user-friendly software: COMPASS - a resonance assignment software for NMR spectroscopy data capable of analyzing chemical shifts and providing the user with suggestions to potential resonance assignments, based on a meticulous database comparison. CDpal - a curve fitting software used to fit thermal and chemical denaturation data of proteins acquired by circular dichroism (CD) spectroscopy or fluorescence spectroscopy. PINT - a line shape fitting and downstream analysis software forNMRspectroscopy data, designed with the important purpose to easily and accurately fit peaks in NMR spectra and extract parameters such as relaxation rates, intensities and volumes of peaks. This thesis also describes a study performed on variants of the life essential regulatory protein calmodulin that have been associated with the congenital life threatening heart disease long QT syndrome (LQTS). The study provided novel insights revealing that all variants are distinct from the wild type in regards to structure and dynamics on a detailed level; the presented results are useful for the interpretation of results from protein interaction studies. The underlying research of this paper makes use of all three developed software, which validates that all developed methods fulfil a scientific purpose and are capable of producing solid results.

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Shankar, Vijay. "Extension of Multivariate Analyses to the Field of Microbial Ecology." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464358122.

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Nabiyouni, Maryam. "Mega-scale Bioinformatics Investigation of Codon Bias in Vertebrates." University of Toledo Health Science Campus / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=mco1308092740.

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Carr, Nicole. "Data Pooling to Identify Differentially Expressed Genes in Lung Cancer of Nonsmokers." University of Toledo Health Science Campus / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=mco1461881266.

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Regan-Fendt, Kelly E. "Integrative Network and Transcriptomics Approach Enables Computational Drug Repurposing and Drug Combination Discovery in Melanoma." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1521209048981327.

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Coombes, Caitlin E. "Validation of clustering solutions for clinical data through biologically meaningful simulations and mixed-distance dissimilarity methods." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1584957936561871.

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Al-Khudhair, Ahmed S. "Anti PD-1/PD-L1 Immunotherapy, New Era in the Fight Against Cancer: Genomic and Transcriptomic Exploration." University of Toledo Health Science Campus / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=mco1568910675816609.

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Wu, Xi. "Ontology-driven Web-based Medical Image Sharing Interface for Epilepsy Research." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1496660866436638.

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Lambert, Caroline L. "Identification and Description of Burkholderia pseudomallei Proteins that Bind HostComplement-Regulatory Proteins via in silico and in vitro Analyses." University of Toledo Health Science Campus / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=mco1533315186098586.

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Schobel, Seth Adam Micah. "The viral genomics revolution| Big data approaches to basic viral research, surveillance, and vaccine development." Thesis, University of Maryland, College Park, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10011480.

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Since the decoding of the first RNA virus in 1976, the field of viral genomics has exploded, first through the use of Sanger sequencing technologies and later with the use next-generation sequencing approaches. With the development of these sequencing technologies, viral genomics has entered an era of big data. New challenges for analyzing these data are now apparent. Here, we describe novel methods to extend the current capabilities of viral comparative genomics. Through the use of antigenic distancing techniques, we have examined the relationship between the antigenic phenotype and the genetic content of influenza virus to establish a more systematic approach to viral surveillance and vaccine selection. Distancing of Antigenicity by Sequence-based Hierarchical Clustering (DASH) was developed and used to perform a retrospective analysis of 22 influenza seasons. Our methods produced vaccine candidates identical to or with a high concordance of antigenic similarity with those selected by the WHO. In a second effort, we have developed VirComp and OrionPlot: two independent yet related tools. These tools first generate gene-based genome constellations, or genotypes, of viral genomes, and second create visualizations of the resultant genome constellations. VirComp utilizes sequence-clustering techniques to infer genome constellations and prepares genome constellation data matrices for visualization with OrionPlot. OrionPlot is a java application for tailoring genome constellation figures for publication. OrionPlot allows for color selection of gene cluster assignments, customized box sizes to enable the visualization of gene comparisons based on sequence length, and label coloring. We have provided five analyses designed as vignettes to illustrate the utility of our tools for performing viral comparative genomic analyses. Study three focused on the analysis of respiratory syncytial virus (RSV) genomes circulating during the 2012- 2013 RSV season. We discovered a correlation between a recent tandem duplication within the G gene of RSV-A and a decrease in severity of infection. Our data suggests that this duplication is associated with a higher infection rate in female infants than is generally observed. Through these studies, we have extended the state of the art of genotype analysis, phenotype/genotype studies and established correlations between clinical metadata and RSV sequence data.

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Mukherjee, Chiranjit. "High Resolution Characterization of the Human Oral Microbiome in Health and Disease." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574678346902957.

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Yu, Jingting. "Methods to Evaluate the Effects of Chromatin Organization in eQTL Mapping and the Effects of Design Factors in Cancer Single-cell Studies." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1554463507829716.

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Zhou, Cong. "Machine Learning Based Protein Identitification and Partial Granger Causality : Novel Bioinformatics Approaches for Proteomics Research." Thesis, University of Sussex, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.505888.

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Albukhnefis, Adil Lateef Mahmood. "Nuclei and Nucleoli Segmentation and Analysis." Kent State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=kent1461260282.

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Nordström, Karl J. V. "Characterization and Evolution of Transmembrane Proteins with Focus on G-protein coupled receptors in Pre-vertebrate Species." Doctoral thesis, Uppsala universitet, Funktionell farmakologi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121696.

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G protein-coupled receptors (GPCRs) are one of the largest protein families in mammals. GPCRs are instrumental for hormonal and neurotransmitter signalling and are important in all major physiological systems of the body. Paper I describes the repertoire of GPCRs in Branchiostoma floridae, which is one of the species most closely related species to vertebrates. Mining and phylogenetic analysis of the amphioxus genome showed the presence of at least 664 distinct GPCRs distributed among all the main families of GPCRs; Glutamate (18), Rhodopsin (570), Adhesion (37), Frizzled (6) and Secretin (16). Paper II contains studies of the Adhesion, Methuselah and Secretin GPCR families in nine genomes. The Adhesion GPCRs are the most complex gene family among GPCRs with large genomic size, multiple introns and a fascinating flora of functional domains. Phylogenetic analysis showed Adhesion group V (that contains GPR133 and GPR144) to be the closest relative to the Secretin family among the groups in the Adhesion family, which was also supported by splice site setup and conserved motifs. Paper III examines the repertoire of human transmembrane proteins. These form key nodes in mediating the cell’s interaction with the surroundings, which is one of the main reasons why the majority of drug targets are membrane proteins. We identified 6,718 human membrane proteins and classified the majority of them into 234 families of which 151 belong to the three major functional groups; Receptors (63 groups, 1,352 members), Transporters (89 groups, 817 members) or Enzymes (7 groups, 533 members). In addition, 74 Miscellaneous groups were shown to include 697 members. Paper IV clarifies the hierarchy of the main families and evolutionary origin of majority of the metazoan GPCR families. Overall, it suggests common decent of at least 97% of the GPCRs sequences found in humans, including all the main families.

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Ramasamy, Adaikalavan. "Increasing statistical power and generalizability in genomics microarray research." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:81ccede7-a268-4c7a-9bf8-a2b68634846d.

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The high-throughput technologies developed in the last decade have revolutionized the speed of data accumulation in the life sciences. As a result we have very rich and complex data that holds great promise to solving many complex biological questions. One such technology that is very well established and widespread is DNA microarrays, which allows one to simultaneously measure the expression levels of tens of thousands of genes in a biological tissue. This thesis aims to contribute to the development of statistics that allow the end users to obtain robust and meaningful results from DNA microarrays for further investigation. The methodology, implementation and pragmatic issues of two important and related topics – sample size estimations for designing new studies and meta-analysis of existing studies – are presented here to achieve this aim. Real life case studies and guided steps are also given. Sample size estimation is important at the design stage to ensure a study has sufficient statistical power to address the stated objective given the financial constraints. The commonly used formula for estimating the number of biological samples, its short-comings and potential amelioration are discussed. The optimal number of biological samples and number of measurements per sample that minimizes the cost is also presented. Meta-analysis or the synthesis of information from existing studies is very attractive because it can increase the statistical power by making comprehensive and inexpensive use of available information. Furthermore, one can also easily test the generalizability of findings (i.e. the extent of results from a particular valid study can be applied to other circumstances). The key issues in conducting a meta-analysis for microarrays studies, a checklist and R codes are presented here. Finally, the poor availability of raw data in microarray studies is discussed here with recommendations for authors, journal editors and funding bodies. Good availability of data is important for meta-analysis in order to avoid biased results and for sample size estimation.

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Vaidya, Priyanka S. "Artificial Intelligence Approach to Breast Cancer Classification." University of Akron / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=akron1240957599.

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Huang, Yidi. "Comprehensive Computational Analysis of Disease-site Microbiome in Patients with Myeloid Malignancy." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1575466186675208.

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Bebek, Gurkan. "Functional Characteristics of Cancer Driver Genes in Colorectal Cancer." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1495012693440067.

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Choudhury, Salimur Rashid, and University of Lethbridge Faculty of Arts and Science. "Approximation algorithms for a graph-cut problem with applications to a clustering problem in bioinformatics." Thesis, Lethbridge, Alta. : University of Lethbridge, Deptartment of Mathematics and Computer Science, 2008, 2008. http://hdl.handle.net/10133/774.

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Clusters in protein interaction networks can potentially help identify functional relationships among proteins. We study the clustering problem by modeling it as graph cut problems. Given an edge weighted graph, the goal is to partition the graph into a prescribed number of subsets obeying some capacity constraints, so as to maximize the total weight of the edges that are within a subset. Identification of a dense subset might shed some light on the biological function of all the proteins in the subset. We study integer programming formulations and exhibit large integrality gaps for various formulations. This is indicative of the difficulty in obtaining constant factor approximation algorithms using the primal-dual schema. We propose three approximation algorithms for the problem. We evaluate the algorithms on the database of interacting proteins and on randomly generated graphs. Our experiments show that the algorithms are fast and have good performance ratio in practice. xiii, 71 leaves : ill. ; 29 cm.

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Nibbe, Rod K. "Systems Biology of Human Colorectal Cancer." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1264179836.

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Gates, Peter. "Development of a model to predict outcomes after dynamic cycling people with Parkinson's disease." Kent State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=kent1625846829132496.

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Wylie, Christi J. "Distinct Transcriptomes Define Rostral and Caudal 5HT Neurons." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1266002785.

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Blankenship, Elise. "Conserved solvent networks in GPCR activation." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1458221506.

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Sinicropi-Yao, Sara Lu-Ming. "The Role of NOTCH1 in Lung Cancer." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1522064811057979.

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Bjarnadóttir, Þóra Kristín. "The Gene Repertoire of G protein-coupled Receptors : New Genes, Phylogeny, and Evolution." Doctoral thesis, Uppsala University, Department of Neuroscience, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6627.

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The superfamily of G protein-coupled receptors (GPCRs) is one of the largest protein families of mammalian genomes and can be divided into five main families; Glutamate, Rhodopsin, Adhesion, Frizzled, and Secretin. GPCRs participate in most major physiological functions, contributing to the fact that they are important targets in drug discovery. In paper I we mined the human and mouse genomes for new Adhesion GPCR genes. We found two new human genes (GPR133 and GPR144) and 17 mouse Adhesion genes, bringing the number up to 33 human and 31 mouse genes. In paper II we describe 53 new splice variants for human Adhesion receptors supported by expressed sequence tags (EST) data. 29 of these variants seem to code for functional proteins, several of which lack one or more functional domains in the N-termini. Lack of certain domains is likely to affect ligand binding or interaction with other proteins. Paper III describes the Glutamate GPCR in human, mouse, Fugu, and zebrafish. We gathered a total of 22 human, 79 mouse, 30 Fugu, and 32 zebrafish sequences and grouped these into eight clans using phylogenetic methods. The report provides an overview of the expansion or deletions among the different branches of the Glutamate receptor family. Paper IV focuses on the trace amine (TA) clan of Rhodopsin GPCRs. We identified 18 new rodent genes, 57 zebrafish genes, and eight Fugu genes belonging to the clan. Chromosomal mapping together with phylogenetic relationships suggests that the family arose through several mechanisms involving tetraploidisation, block duplications, and local duplication events. Paper V provides a comprehensive dataset of the GPCR superfamily of human and mouse containing 495 mouse and 400 human non-olfactory GPCRs. Phylogenetic analyses showed that 329 of the receptors are found in one-to-one orthologous pairs, whereas other receptors may have originated from species-specific expansions.

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Ghosh, Subhanwita. "Regulatory Mechanisms of Cardiotonic Steroids in Chronic Kidney Disease." University of Toledo Health Science Campus / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=mco1501858848160683.

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Holtzapple, Emilee R. "RelA as a Potential Regulator of Inflammation and Tissue Damage in Streptozotocin-Induced Diabetic STAT5 Knockout Mice." Ohio University Honors Tutorial College / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1461342848.

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Jahn, Katharina [Verfasser]. "Approximate common intervals based gene cluster models / Katharina Jahn. International NRW Graduate School in Bioinformatics and Genome Research -- Center for Biotechnology (CeBiTec). Technische Fakultät." Bielefeld : Universitätsbibliothek Bielefeld, Hochschulschriften, 2011. http://d-nb.info/1012868842/34.

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Zelinka, Lisa M. "PROTEIN EXPRESSION AND CHARACTERIZATION OF THE MAJOR AUTOANTIGEN (TITIN DOMAIN) ASSOCIATED WITH AUTOIMMUNERIPPLING MUSCLE DISEASE." Kent State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=kent1416852571.

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Tomkova, Marketa. "The relationship between DNA modifications and mutations in cancer." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:013eed80-45e9-48b9-84a7-718c6111e407.

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Somatic mutations are the main triggers that initiate the formation of cancer. Large sequencing data sets in recent years revealed a substantial number of mutational processes, many of which are poorly understood or of completely unknown aetiology. These mutational processes leave characteristic sequence patterns, often called "signatures", in the DNA. Characterisation of the mutational patterns observed in cancer patients with respect to different genomic features and processes can help to unravel the aetiology and mechanisms of mutagenesis. Here, we explored the effects of DNA modifications and DNA replication on mutagenesis. The most common mutation type, C&GT;T mutations in a CpG context, is thought to result from spontaneous deamination of 5-methylcytosine (5mC), the major DNA modification. Much less is known about the mutational properties of the second most frequent modification, 5-hydroxymethylcytosine (5hmC). Integrating multiple genomic data sets, we demonstrate a twofold lower mutagenicity of 5hmC compared to 5mC, present across multiple tissues. Subsequently, we show how DNA modifications may modulate various mutational processes. In addition to spontaneous deamination of 5mC, our analysis suggests a key role of replication in CpG > TpG mutagenesis in patients deficient in post-replicative proofreading or repair, and possibly also in other cancer patients. Together with an analysis of mutation patterns observed in cancers exposed to UV light, tobacco smoke, or editing by APOBEC enzymes, the results show that the role of DNA modifications goes beyond the well-known spontaneous deamination of 5mC. Finally, we explored which of the known mutational processes might be modulated by DNA replication. We developed a novel method to quantify the magnitude of strand asymmetry of different mutational signatures in individual patients followed by evaluation of these exposures in early and late replicating regions. More than 75 % of mutational signatures exhibited a significant replication strand asymmetry or correlation with replication timing. The analysis gives new insights into mechanisms of mutagenicity in multiple signatures, particularly the so far enigmatic signature 17, where we suggest an involvement of oxidative damage in its aetiology. In conclusion, our results suggest that DNA replication or replication-associated DNA repair interacts with most mutagenic processes.

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Kim, Jaeyeon. "Model Analysis of Adipose Tissue and Whole Body Metabolism In Vivo." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1216436630.

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Paul, Reeba. "Molecular Evolution of CTL Epitopes in HIV-1: Understanding Geographic Variations." Kent State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=kent1471438387.

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Prager, Briana C. "THE MENINGIOMA ENHANCER LANDSCAPE DELINEATES PROGNOSTIC SUBGROUPS AND DRIVES DRUGGABLE DEPENDENCIES." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1595620620551252.

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Jones, Derek. "Scalable Feature Selection and Extraction with Applications in Kinase Polypharmacology." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/65.

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In order to reduce the time associated with and the costs of drug discovery, machine learning is being used to automate much of the work in this process. However the size and complex nature of molecular data makes the application of machine learning especially challenging. Much work must go into the process of engineering features that are then used to train machine learning models, costing considerable amounts of time and requiring the knowledge of domain experts to be most effective. The purpose of this work is to demonstrate data driven approaches to perform the feature selection and extraction steps in order to decrease the amount of expert knowledge required to model interactions between proteins and drug molecules.

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Thiel, Bonnie Arlene. "Bioinformatics approaches to studying immune processes associated with immunity to Mycobacterium tuberculosis infection in the lung and blood." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1627247387242562.

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Frankhouser, David E. "Methylome Analysis: From Computation Workflow Development to Implementation in a Breast Cancer Prevention Trial." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512052081030923.

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Wang, Yuepeng. "Integrative methods for gene data analysis and knowledge discovery on the case study of KEDRI's brain gene ontology a thesis submitted to Auckland University of Technology in partial fulfilment of the requirements for the degree of Master of Computer and Information sciences, 2008 /." Click here to access this resource online, 2008. http://hdl.handle.net/10292/467.

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Saha, Mandal Arnab. "Computational Analysis of the Evolution of Non-Coding Genomic Sequences." University of Toledo Health Science Campus / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=mco1372349811.

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Slaibi, Jinani Elias. "Targets of Hsa-miR-488* In Human Prostate Carcinoma Cells." Cleveland State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=csu1273843449.

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Piñero, González Janet 1977. "Computational approaches and resources to support translational research in human diseases." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/328417.

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In the last two decades, the volume and variety of biomedical data has dramatically increased. The data is heterogeneous and scattered across many resources. This produces bottlenecks in the analysis and extraction of knowledge from this sea of information. To overcome this hurdle, better catalogs that integrate different data types, offer easy access to users, and support automatic workflows, are needed. With this in mind, we have developed DisGeNET, a discovery platform that contains information on more than 17,000 genes related to over 14,000 diseases. We have used DisGeNET to study the properties of disease genes in the context of protein interaction networks. To produce an accurate analysis of the mesoscale properties of the human interactome, we first compared the network partitions generated by two popular clustering algorithms, to assess how this would impact the follow-up biological analysis. Using the best performing algorithm we then explored the network properties of disease genes. Then we evaluated the relationship between the network properties of different groups of disease genes and their tolerance to likely deleterious germline variants across human populations. Finally, we have developed a new network medicine approach to study disease comorbidities, and applied it to the analysis of COPD comorbidities. Los avances tecnológicos de las últimas dos décadas han producido un incremento dramático en la cantidad y la diversidad de datos biomédicos disponibles. Este proceso ha ocurrido de manera fragmentada, y en consecuencia los datos se encuentran almacenados en distintos repositorios, lo cual impone barreras a la hora de integrarlos, analizarlos y extraer conocimiento a partir de ellos. Para superar estas barreras, es necesario contar con recursos computacionales que integren esta información, y ofrezcan un fácil acceso a la misma, permitiendo al mismo tiempo su análisis automatizado. En respuesta a esta necesidad hemos desarrollado DisGeNET, una plataforma orientada a la exploración de las causas genéticas de las enfermedades humanas, que contiene actualmente información sobre más de 14.000 enfermedades y 17.000 genes. En esta tesis, describimos el uso de DisGeNET para el estudio de las propiedades de los genes asociados a enfermedades en el contexto de redes de interacción entre proteínas. Para ello, evaluamos previamente cómo la utilización de distintos algoritmos de reconocimiento de comunidades en redes afecta a los resultados de los análisis e influencia su interpretación biológica. A continuación, caracterizamos las propiedades de redes de los genes asociados a enfermedades como conjunto y también en sub-grupos, empleando diferentes criterios de clasificaciones de las enfermedades. Posteriormente, evaluamos cómo estas propiedades están relacionadas con la tolerancia a mutaciones posiblemente deletéreas en distintos grupos de genes, mediante el análisis de datos generados por las nuevas tecnologías de secuenciación. Finalmente, desarrollamos una nueva metodología de medicina de sistemas para explorar los mecanismos moleculares de la comorbilidades, y la aplicamos al estudio de las comorbilidades de la enfermedad pulmonar obstructiva crónica

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47

Prescott, Jeffrey William. "Computer-assisted discovery and characterization of imaging biomarkers for disease diagnosis and treatment planning." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1280194844.

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48

Grigsby, Claude Curtis. "A Comprehensive Tool and Analytical Pathway for Differential Molecular Profiling and Biomarker Discovery." Wright State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1387540709.

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Cannon, Matthew. "Large-scale Investigation of Fetal Hemoglobin Modulators and Inflammation Biomarkers in Sickle Cell Disease." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu16188574420699.

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50

Choi, Ickwon. "Computational Modeling for Censored Time to Event Data Using Data Integration in Biomedical Research." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1307969890.

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Dissertations / Theses on the topic 'Bioinformatics – Research'

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