Sparse neural network for modality selection of multi-omic data
In this master's thesis, we will develop and utilize a novel sparse deep learning framework designed to perform simultaneous prediction and modality selection applied to microbiome and metabolite datasets. You can read the full proposal here
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Predicting the effect of drug perturbations on the human gut microbiome
This project aims to develop predictive models that can assess a compound's impact on the human gut microbiome. You can read the full proposal here
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Proteome based bacterial representations
By reducing embedding redundancy and incorporating supervised fine-tuning, we aim to produce efficient protein representations for a better microbial representation to enhance downstream analyses. You can read the full proposal here
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Extending DGrowthR with Penalized Spline Models for Microbial Growth Curve Analysis
Here we will extend the DGrowthR package by integrating penalized spline models as an alternative to Gaussian processes for growth curve analysis. You can read the full proposal here
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Analyzing Divergent Growth Outcomes in B. subtilis Populations with Varying Initial Cell Densities
Here we will develop analytical methods for characterizing and predicting divergent growth outcomes in B. subtilis cultures based on their initial cell densities. You can read the full proposal here
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Extending DGrowthR to DCurveR: Analysis of Luminescence and Thermal Proteome Profiling Curves
The purpose of this M.Sc. thesis project is to extend DGrowthR into a more general curve analysis package called DCurveR that can handle luminescence curves and thermal proteome profiling data.. You can read the full proposal here
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Hierarchical clustering of bacterial single cells with protein information
We aim to enhance bacterial single-cell analysis by developing a proteome-based representation. You can read the full proposal here
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“Biomedical Statistics and Data Science”