Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 28 for “"Multi-Omics Data"”.
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High-dimensional Mediation Analysis of Multi-Omics Data
… technologies have made mediation analysis of multi-omics data critical to gain groundbreaking insights into the biological mechanisms underlying the disease etiology. This dissertation aims to develop mediation analysis methods that utilize the enormous amount of multi-omics data in assessing …
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Characterization of cancer and aging using multi-omics data
노화는 암을 포함한 많은 질병의 원인이 된다. 인간의 다양한 조직으로부터 추출한 DNA 메틸값을 이용해 실제 나이를 예측할 수 있다. 이는 DNA 메틸화가 노화의 지표가 될 수 있다는 의미이기도 하다. 하지만, 여러 조직의 정상과 종양 샘플의 많은 데이터 셋을 통합하여 노화 관련 DNA 메틸화 영역의 특징에 대해 밝힌 연구는 아직 많이 없는 실정이다. 본 연구에서는 16개 독립적인 연구의 DNA메틸화 및 유전자 발현 데이터를 통합하여 말초 혈액뿐만 아니라 유방, 자궁경부, 전립선, 뇌, 간, 대장 조직 등 다양한 조직의 대략 …
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Multi-omics Data Integration for Identifying Disease Specific Biological Pathways
… 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 …
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Non-linear Multi Omics Data Integration Method Using Conditional Variational Autoencoders
… have enabled the study of diseases through multi-omics data, which combines information from genome, epigenome, transcriptome, proteome, and metabolome levels. Unlike single-omics approaches that provide limited insights, multi-omics integration offers a comprehensive understanding of …
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Generalization of kernel machine methods for association testing of multi-omics data
… of trait etiology. Studies utilizing omics, including transcriptomics, proteomics, metabolomics, etc, are gaining popularity, and, used in conjunction with genomics, may aid in providing insight into complex trait etiology and disease pathogenesis. To fully harness the availability of …
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Statistical methods for the integrative analysis of single-cell multi-omics data
… Recently, technological advances have enabled multiple biological layers to be probed in parallel one cell at a time, unveiling a powerful approach for investigating multiple dimensions of cellular heterogeneity. However, the increasing availability of multi-modal data sets needs to be …
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Inferring effective cancer combination therapies using network-based multi-omics data integration
… utilising a comprehensive drug combination dataset and well-characterised cancer cell line genomic, transcriptomic, proteomic and methylation data. We hypothesised that (i) drug efficacy as measured by cancer cell viability is adequate, and synergy between drugs is not necessarily required …
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Mixture Model Approaches to Integrative Analysis of Multi-Omics Data and Spatially Correlated Genomic Data
<p>Integrative genomic data analysis is a powerful tool to study the complex biological processes behind a disease. Statistical methods can model the interrelationships of the involved gene activities through jointly analyzing multiple types of genomic data from different platforms (vertical …
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The influence of the inclusion of biological knowledge in statistical methods to integrate multi-omics data
… has led to the explosion of biological data available for analysis, allowing researchers to investigate multiple molecular layers (i.e. omics data) together. The classical statistical methods could not address the challenges of combining multiple data types, leading to the development of …
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Development of Soybean Knowledge Base (SoyKB), a multi-omics data integration web resource for bridging molecular breeding and translational genomics in Glycine Max
… AT AUTHOR'S REQUEST.] Many genome-scale data are available in soybean (Glycine Max) including genomics, transcriptomics, proteomics and metabolomics datasets, together with growing knowledge of soybean in gene, microRNAs, pathways, and phenotypes. This represents rich and resourceful …
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Differential Dependency Network and Data Integration for Detecting Network Rewiring and Biomarkers
… profiling techniques enabled large-scale genomics, transcriptomics, and proteomics-based biomedical studies, generating an enormous amount of multi-omics data. Processing and summarizing multi-omics data, modeling interactions among biomolecules, and detecting condition-specific dysregulation …
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Machine learning enabled bioinformatics tools for analysis of biologically diverse samples
… decades, the generation of vast volumes of multi-omics data, spanning a broad range of phenotypes. Development of advanced bioinformatics tools to identify informative biomarkers from these data becomes increasingly important. These tools are crucial to extract meaningful biomarkers from …
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Understanding neurodegenerative disease-relevant molecular effects of perturbagens using a multi-omics approach
… metabolomic, epigenomic, and proteomic data ("multi-omics" data). Our studies revealed novel modes of action for small molecule compounds that promote survival in a model of Huntington's Disease, a fatal neurodegenerative disorder. Integration of our multi-omics data using an …
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A penalized linear mixed model with generalized method of moments estimators for complex phenotype prediction
… for risk prediction analysis on high-dimensional data, where random effect terms are used to capture predictive effects from multiple markers. However, it remains computationally challenging to simultaneously model a large number of variables that can be noise or have predictive effects of complex …
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Functional interpretation of cancer-associated genetic variants
… of heterogeneous high-throughput sequencing data. Application of the method to breast cancer susceptibility regions reveals functional variants and their perturbation on cis-regulatory elements that act on cancer-associated genes. It is also shown that a cancer-associated variant may interact …
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PATIENT SIMILARITY NETWORKS-BASED METHODS FOR MULTIMODAL DATA INTEGRATION AND CLINICAL OUTCOME PREDICTION
… relies on the ability to collect comprehensive data from each patient, covering various aspects of their disease. This includes gathering information at different levels to form a complete picture of the pathology, incorporating genomic, environmental, and lifestyle factors. Recent technological …
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A Study of Machine Learning Approaches for Integrated Biomedical Data Analysis
… analysis for the integration of biomedical data analysis were explored, developed and tested. Integration of different biomedical data sources allows us to get a better understating of human body from a bigger picture. If we can get a more complete view of the data, we not only get a more …
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Pan-Cancer Analysis of RNA Dysregulation, Somatic Mutations, and Matrix Stiffness using Bioinformatics Approaches
… level. This dissertation leverages RNA-seq data to explore different dimensions of cancer biology through multi-omics integration and computational approaches. The research is divided into three main projects. First, I developed OncoDB, an interactive database to analyze gene expression, …
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Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis
… and high-dimension, low-sample size (HDLSS) data cause computational challenges in survival analysis. We developed a novel family of pathway-based, sparse deep neural networks (PASNet) for cancer survival analysis. PASNet family is a biologically interpretable neural network model where nodes …
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Informatics Strategies for Alzheimer’s Disease Research Through Analyzing Genetics and Neuroimaging Data
… employing informatics strategies within the Genomics, Molecular Multiomics, Biomarkers, and Outcomes (GMBO) framework to integrate large-scale biobank data and uncover key disease mechanisms. Through computational approaches, this work bridges genetic, molecular, and neuroimaging data to address …
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