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proposals.html
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<h1><a href="#">Project proposals</a></h1>
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<p>There is never a shortage of ideas and project proposals in our group
<br>
Take a look at some of the ideas we have for Master students.
If something cathces your eye, get in contact with us!
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<span class="byline"><p style="font-size:23px"><strong><a target="_blank" href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MScProposal_CovidTensor.pdf">Analyzing covid-19 data as three-dimensional tensor</a></strong></p></span>
<h6 align="justify">
The purpose of this M.Sc. thesis proposal is to retrospectively analyze the covid-19
incidence data from the German Robert-Koch-Intitut (RKI) in three dimensions: age, region and time.
This makes it possible to show which age groups were infected at which point in time in which parts of Germany.
To uncover age specific spatio-temporal patterns from the data, we consider the incidence data as a three-dimensional
tensor and apply non-negative tensor factorization.
You can read the full proposal <strong><a href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MScProposal_CovidTensor.pdf" target="_blank">here</a></strong>
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<span class="byline"><p style="font-size:23px"><strong><a target="_blank" href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MSCThesis_compositional_software.pdf">Integration and visualization of compositional cell population data</a></strong></p></span>
<h6 align="justify">
High-throughput RNA sequencing technologies, such as 16S rRNA and single-cell
RNA sequencing, allow us to determine the type of every cell or microorganism
in a biological sample. Unfortunately, different technologies use different
processing pipelines and data formats on the gene level, often limiting
implementations of novel analysis methods on the cell level to one pipeline.
Also, visualization of such data must be done carefully due to its
compositional nature. <br>
The goal of this M.Sc. Thesis is to develop a data integration and
visualization package in Python to allow for unified analysis of these
types of data. You can read the full proposal <strong><a href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MSCThesis_compositional_software.pdf" target="_blank">here</a></strong>
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<span class="byline"><p style="font-size:23px"><strong><a target="_blank" href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MScProposal_microbiome_cardiovascular.pdf">Learning graphical models to explore the relationship between human gut microbiota and cardiovascular diseases</a></strong></p></span>
<h6 align="justify">
Many diseases are multifactorial in origin, meaning that they are caused by
a combination of genetic and environmental components. These independent genetic
factors are often common, occurring frequently in the absence of disease,
and therefore cannot yet be used to predict disease. The role that the gut
microbiome plays in these diseases remains unexplored. <br>
The purpose of this M.Sc. thesis is to explore the relationship between microbiome
composition and cardiovascular disease using state-of-art machine learning and
statistical methods. You can read the full proposal <strong><a href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MScProposal_microbiome_cardiovascular.pdf" target="_blank">here</a></strong>
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<span class="byline"><p style="font-size:23px"><strong><a target="_blank" href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MScProposal_sRNA_prediction.pdf">Prediction of short RNA regulatory targets in <i>Campylobacter jejuni</i></a></strong></p></span>
<h6 align="justify">
Previous studies have shown that short RNA (sRNA) molecules play an
important role in the regulation of certain bacterial processes,
such as virulence. They achieve this by inhibiting the expression of their
target genes.
At the moment, there are few known regulatory targets for
the sRNAs of the human pathogen <i>C. jejuni</i>.
Furthermore, the biological pathways in which they participate are also unknown.
<br>
The purpose of this M.Sc. thesis is to predict the regulatory targets of
sRNA molecules in <i>C. jejuni</i> and to assess their biological role.
You can read the full proposal <strong><a href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MScProposal_sRNA_prediction.pdf" target="_blank">here</a></strong>
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<span class="byline"><strong><p style="font-size:23px"><a target="_blank" href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MSCThesis_scCODA_extension.pdf">Bayesian modeling of high-throughput sequencing data</a></strong></p></span>
<h6 align="justify">
One of the products of our group is scCODA. scCODA is a Bayesian model
can identify compositional changes of cell types. The scCODA model was
specifically designed for use in very low-dimensional settings,
where some properties of high- throughput sequencing data
like zero-inflation and overdispersion are less pronounced. <br>
The goal of this M.Sc. Thesis is to extend this framework, so that it can be
used with higher-dimensional data.
You can read the full proposal <strong><a href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MSCThesis_scCODA_extension.pdf" target="_blank">here</a></strong>
</h6>.
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<span class="byline"><strong><p style="font-size:23px"><a target="_blank" href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MScProposal_alpha.pdf">Alpha diversity measures for microbiome data</a></strong></p></span>
<h6 align="justify">
Complex microbiome samples can be summarized into a
single measure by alpha diversity indices, characterizing
the structure of a community in (microbial) ecology.
To date, a wide variety of alpha diversity measures have
been developed, ranging from traditional diversity
estimates from macro-ecology to recently developed
microbiome-specific measures.
With this MSc thesis, we would like to review the
appropriateness of traditional and new alpha diversity
measures for microbiome data from a statistical point of
view, and to compare these diversity measures on mock and
clinical microbiome data.
You can read the full proposal <strong><a href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MScProposal_alpha.pdf" target="_blank">here</a></strong>
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