Search for dissertations about: "Transcriptomics"
Showing result 11 - 15 of 169 swedish dissertations containing the word Transcriptomics.
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11. Computational methods for analysis of spatial transcriptomics data : An exploration of the spatial gene expression landscape
Abstract : Transcriptomics techniques, whether in the form of bulk, single cell/nuclei, or spatial methods have fueled a substantial expansion of our knowledge about the biological systems within and around us. In addition, the rate of innovation has accelerated over the last decade, resulting in a multitude of technological advances and new methods for generation of transcriptomics data. READ MORE
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12. Integration of RNA and protein expression profiles to study human cells
Abstract : Cellular life is highly complex. In order to expand our understanding of the workings of human cells, in particular in the context of health and disease, detailed knowledge about the underlying molecular systems is needed. READ MORE
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13. Genomic and transcriptomic investigation of reproductive incompatibility in Drosophila
Abstract : Both nuclear and cytoplasmic elements can contribute to the emergence of reproductive incompatibilities that influence evolution and speciation. In the projects that compose this thesis, we use genomics and transcriptomics to study some of those elements in Drosophila. READ MORE
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14. Function and Evolution of Small Regulatory RNAs and their Associated Proteins : A Journey from Genome to Proteome
Abstract : Organisms throughout the tree of life have evolved distinct ways to regulate gene expression. Some of these processes involve non-coding RNAs (ncRNAs), which are not translated but functional nonetheless. These ncRNAs are of utmost importance, with dysregulation of some causing severe developmental effects or even being lethal. READ MORE
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15. Patterns in big data bioinformatics : Understanding complex diseases with interpretable machine learning
Abstract : Alterations in the flow of genetic information may lead to complex diseases. Such changes are measured with various omics techniques that usually produce the so-called “big data”. Using interpretable machine learning (ML), we retrieved patterns from transcriptomics data sets. READ MORE