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Dissertations / Theses on the topic 'Gene set enrichment analyses'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Paszkowski-Rogacz, Maciej, Frank Buchholz, Mikolaj Slabicki, and Maria Teresa Pisabarro. "PhenoFam-gene set enrichment analysis through protein structural information." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-176848.

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Background With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, Phen

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Paszkowski-Rogacz, Maciej, Frank Buchholz, Mikolaj Slabicki, and Maria Teresa Pisabarro. "PhenoFam-gene set enrichment analysis through protein structural information." BioMed Central, 2010. https://tud.qucosa.de/id/qucosa%3A28875.

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Background With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, Phen

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3

Li, Wei. "Analyzing Gene Expression Data in Terms of Gene Sets: Gene Set Enrichment Analysis." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/79.

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The DNA microarray biotechnology simultaneously monitors the expression of thousands of genes and aims to identify genes that are differently expressed under different conditions. From the statistical point of view, it can be restated as identify genes strongly associated with the response or covariant of interest. The Gene Set Enrichment Analysis (GSEA) method is one method which focuses the analysis at the functional related gene sets level instead of single genes. It helps biologists to interpret the DNA microarray data by their previous biological knowledge of the genes in a gene set. GSEA

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4

Kodysh, Yuliya. "Using co-expression to redefine functional gene sets for gene set enrichment analysis." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41661.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.<br>Includes bibliographical references (p. 89-90).<br>Manually curated gene sets related to a biological function often contain genes that are not tightly co-regulated transcriptionally. which obscures the evidence of coordinated differential expression of these gene sets in relevant experiments. To address this problem, we explored strategies to refine the manually curated subcollection of the Molecular Signatures Database (MSigDB) for use with Gene Set Enrichment Analysis (GSE

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5

Jadhav, Trishul. "Knowledge Based Gene Set analysis (KB-GSA) : A novel method for gene expression analysis." Thesis, University of Skövde, School of Life Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-4352.

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<p>Microarray technology allows measurement of the expression levels of thousand of genes simultaneously. Several gene set analysis (GSA) methods are widely used for extracting useful information from microarrays, for example identifying differentially expressed pathways associated with a particular biological process or disease phenotype. Though GSA methods like Gene Set Enrichment Analysis (GSEA) are widely used for pathway analysis, these methods are solely based on statistics. Such methods can be awkward to use if knowledge of specific pathways involved in particular biological processes a

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6

Lu, Yingzhou. "Multi-omics Data Integration for Identifying Disease Specific Biological Pathways." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83467.

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Pathway analysis is an important task for gaining novel insights into the molecular architecture of many complex diseases. With the advancement of new sequencing technologies, a large amount of quantitative gene expression data have been continuously acquired. The springing up omics data sets such as proteomics has facilitated the investigation on disease relevant pathways. Although much work has previously been done to explore the single omics data, little work has been reported using multi-omics data integration, mainly due to methodological and technological limitations. While a single om

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7

SARTOR, MAUREEN A. "TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195656673.

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8

Yu, Mengyao. "Exploitation des données issues d'études d'association pangénomiques pour caractériser les voies biologiques associées au risque génétique du prolapsus de la valve mitrale GWAS-driven gene-set analyses, genetic and functional follow-up suggest GLIS1 as a susceptibility gene for mitral valve prolapse Up-dated genome-wide association study and functional annotation reveal new risk loci for mitral valve prolapse." Thesis, Sorbonne Paris Cité, 2019. https://wo.app.u-paris.fr/cgi-bin/WebObjects/TheseWeb.woa/wa/show?t=2203&f=17890.

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Le prolapsus de la valve mitrale (MVP) est une valvulopathie fréquente qui touche près de 1 personne sur 40 dans la population générale. Il s'agit de la première indication de réparation et / ou de remplacement de la valve. De nombreux gènes comme FLNA, DCHS1 pour les formes familiales et TNS1 et LMCD1 pour les formes sporadiques ont récemment été décrit comme associés au MVP. Cependant, les défauts génétiques touchant ces gènes n'expliquent pas tous les cas du MVP. De plus, les mécanismes biologiques expliquant la susceptibilité génétique au MVP, notamment pour les formes sporadiques les plus

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9

DADOUSIS, CHRISTOS. "From milk to cheese: genomic background, biological pathways and latent phenotypes of bovine cheese-related traits." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3424728.

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The aim of this PhD thesis was the study of the genomic, biological and phenotypic background of bovine cheese-related traits. The primary goal of this PhD thesis was to unravel the genomic background of bovine milk technological and cheese-related traits to specific chromosomic regions (CHAPTERS 1 to 3). To achieve this, the cow’s ability to produce cheese was decomposed into 11 milk coagulation (MCP) and curd-firming properties (CFt), and 7 cheese yield and milk component recoveries into the curd (REC) traits. Besides, to tackle the problem of the large number of variables required to descri

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10

Martini, Paolo. "Dissecting the transcriptome complexity with bioinformatics tools." Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3422923.

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Bioinformatics has acquired a lot of importance especially with the advent of genomic approaches. The large amount of data produced by ``omics'' experiments requires appropriate frameworks to handle, store and mine the information and to derive appropriate work hypotheses. Transcriptome is defined as the whole amount of RNA molecules produced by a cell that provides the bridge between the genome and proteins. RNA molecules can be divided in two major classes: protein coding RNAs or messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs). While the first class has been the most studied in the last

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11

Hänzelmann, Sonja 1981. "Pathway-centric approaches to the analysis of high-throughput genomics data." Doctoral thesis, Universitat Pompeu Fabra, 2012. http://hdl.handle.net/10803/108337.

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In the last decade, molecular biology has expanded from a reductionist view to a systems-wide view that tries to unravel the complex interactions of cellular components. Owing to the emergence of high-throughput technology it is now possible to interrogate entire genomes at an unprecedented resolution. The dimension and unstructured nature of these data made it evident that new methodologies and tools are needed to turn data into biological knowledge. To contribute to this challenge we exploited the wealth of publicly available high-throughput genomics data and developed bioinformatics methodo

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12

Stamm, Karl D. "Gene set enrichment and projection| A computational tool for knowledge discovery in transcriptomes." Thesis, Marquette University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10146411.

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<p> Explaining the mechanism behind a genetic disease involves two phases, collecting and analyzing data associated to the disease, then interpreting those data in the context of biological systems. The objective of this dissertation was to develop a method of integrating complementary datasets surrounding any single biological process, with the goal of presenting the response to a signal in terms of a set of downstream biological effects. This dissertation specifically tests the hypothesis that computational projection methods overlaid with domain expertise can direct research towards relevan

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13

Douglass, James F. "Biomineralization of atrazine and analysis of 16S rRNA and catabolic genes of atrazine-degraders in a former pesticide mixing and machinery washing area at a farm site and in a constructed wetland." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440373757.

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14

Li, Pei-Hsun, and 李沛洵. "Gene Set Enrichment Analysis of RNA-Seq data." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/37077367052846883613.

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碩士<br>國立臺灣大學<br>農藝學研究所<br>104<br>During the past few years, RNA-Seq technology has been widely employed for studying the transcriptome since it has clear advantages over the other transcriptomic technologies. The most popular use of RNA-seq applications is to identify differentially expressed genes. In addition, gene set analysis (GSA) aims to determine whether a predefined gene set, in which the genes share a common biological function, is correlated with the pheno-type. To date, many GSA approaches have been developed for identifying differentially expressed gene sets using microarray data.

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15

Zhao, Kaiqiong. "Gene-pair based statistical methods for testing gene set enrichment in microarray gene expression studies." 2016. http://hdl.handle.net/1993/31796.

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Gene set enrichment analysis aims to discover sets of genes, such as biological pathways or protein complexes, which may show moderate but coordinated differentiation across experimental conditions. The existing gene set enrichment approaches utilize single gene statistic as a measure of differentiation for individual genes. These approaches do not utilize any inter-gene correlations, but it has been known that genes in a pathway often interact with each other. Motivated by the need for taking gene dependence into account, we propose a novel gene set enrichment algorithm, where the gene-g

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16

Tang, Yu-Chuan, and 湯育全. "Methods based on distance statistics for detection of differentially expressed genes and gene set enrichment analysis." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4bnub4.

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碩士<br>國立臺灣大學<br>農藝學研究所<br>107<br>The first part of this paper is to study the effectiveness of differentially expressed gene analysis. Statistical methods such as t-test or SAM treat each gene as independent and separately identify whether it is a differentially expressed gene. However, the results of the test may be biased because of the correlation between genes. Therefore, a novel statistic called OR value is proposed for identifying differentially expressed genes recently. The advantage of OR value is no model assumptions and no estimated parameters, as well as the Euclidean distance is us

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17

Huang, Hui-Jun, and 黃惠君. "Gene Set Enrichment Analysis of microRNA Functional Roles in Biological Network and System Implementation." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/64648769163050158297.

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碩士<br>國立成功大學<br>資訊工程學系碩博士班<br>97<br>In recent years, DNA microarrays have been widely not only applied on gene functional role analysis but also supported on interaction information across different species. Furthermore, it has the advantage of quickly obtaining gene profiles through whole genome. However, how to evaluate gene expression level is still a tough problem. To overcome this problem, Gene Set Enrichment Analysis (GSEA) was proposed in 2005 for better interpreting microarray expression data. GSEA focus on gene sets, groups of genes that share common biological concepts. Based on GSEA

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18

Chen, Ching-yi, and 陳靜怡. "Investigation of Microarray Data Using Gene Set Enrichment Analysis - Arabidopsis thaliana infected with Xanthomonas campestris pv. campestris." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/73355292561541750960.

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碩士<br>亞洲大學<br>生物與醫學資訊學系碩士在職專班<br>100<br>Gaining a better understanding of the biotic and abiotic stress responses for plant systems provide a model system for studying human diseases and drug-related research. Understanding how plant systems defense against environment stress is of great significance for the world's food and agricultural production.In this study, the microarray data for Arabidopsis thaliana infected with Xanthomonas campestris pv. campestris （Xcc） is analyzed. Microarray data for Arabidopsis infected with Xcc are retrieved from the ArrayExpress database, where differentially e

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19

Tsai, Hsin-Ying, and 蔡欣穎. "Comparison of statistical methods for gene set enrichment tests." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/06789559500820048779.

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碩士<br>國立臺灣大學<br>農藝學研究所<br>102<br>Microarray aims to simultaneously monitor the expression of thousands of genes. It is usually the objective to mine important information from the data, such as the representative genes that differentially expressed (DE) under different conditions. In recent years, several gene set enrichment tests have been proposed to search for a DE gene set under different conditions. The gene set enrichment tests can be divided into two categories, univariate and multivariate methods. The former summarizes univariate statistics from each gene in the set to infer whether th

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20

Bayerlová, Michaela. "Pathway and network analyses in context of Wnt signaling in breast cancer." Doctoral thesis, 2016. http://hdl.handle.net/11858/00-1735-0000-0028-86E1-F.

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