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Biharie, Kirti (author)
Knowing the relation between cell types is crucial for translating experimental results from mice to humans. Establishing cell type matches, however, is hindered by the biological differences between the species. A substantial amount of evolutionary information between genes that could be used to align the species, is discarded by most of the...
master thesis 2022
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Drummer, Francesca (author)
Single-cell sequencing allows measuring individual cells' molecular features and their responses to perturbations. Understanding which cells respond to a particular perturbation and how these responses vary across populations can be used to, for example, improve vaccine immunogenicity. However, an exhaustive exploration of single-cell...
master thesis 2022
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Mikuš, Matus (author)
master thesis 2022
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Singh, Akash (author)
Single-cell multi-modal omics promises to open new doors in bioinformatics by measuring different aspects of cells, thus offering multiple perspectives on the underlying biological phenomenon. Although simultaneous multi-modal measurement protocols do exist, their inherent technical limitations necessitate focus on single modality measurements....
master thesis 2021
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Michielsen, Lieke (author)
Since the revolution of single-cell RNA-sequencing, the number of available datasets has increased enormously. In these datasets, cell identification is mainly done manually, which is subjective and time-consuming. As a consequence, most datasets are annotated at a different resolution. This is not surprising as cell types form a hierarchy, but...
master thesis 2020
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