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 5 of 5 for “"Multi-omics Data Integration"”.
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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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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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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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Machine learning and data analytics for liver disease modeling
… of computational methodologies, including data-driven machine learning models, novel hybrid frameworks that integrate mechanistic and learning-based components, and multi-omics data integration. We begin by assessing the limitations of conventional machine learning approaches in predicting …