Projects: COVID-19 Disease Map
Institutions: Monash University

Roles: PhD Student
Expertise: Bioinformatics, Machine Learning, Data analysis, Deep Learning, Molecular Biology, Plant biology
Tools: Python, R, SQL, High Performance Computing
Projects: COVID-19 Disease Map, Covid-19 Interferon pathway modelling and analysis
Institutions: Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg

Expertise: Machine Learning, Deep Learning, Modeling, Medical microbiology, Molecular Biology
Tools: Python, R, Matlab, Javascript, SBML, Dynamic modelling, microbiology techniques, Shell scripting
Projects: COVID-19 Disease Map
Institutions: Biomax Informatics AG
Expertise: Systems Biology, SBML, Java, Python, Machine Learning, Mathematical modelling, SBGN, Curation
Tools: CellDesigner, SBML, SBGN
Projects: COVID-19 Disease Map
Institutions: Biomax Informatics AG
Projects: iRhythmics
Institutions: University of Rostock
Roles: PhD Student
Expertise: Machine Learning, Deep Learning
Expertise: Software Engineering, Machine Learning
Tools: Python, cobrapy toolbox
Projects: SUMO
Institutions: University of Edinburgh
Expertise: Machine Learning, Bioinformatics, Computational modelling
Tools: Bayesian inference
Projects: TRANSLUCENT
Institutions: University of Applied Sciences Koblenz, Rhein Ahr Campus
Expertise: Ma
Tools: Computational and theoretical biology, Matlab, R, Biophysics, Thermodynamics, Machine Learning
I am working in the mathematical modeling of potassium homeostasis. In addition, we are developing tools and methods for the statistical analysis of biological data.
Application of the LoRAS oversampling approach on single-cell/single-nuclei data to annotate/identify specific cell populations in new data based on previously, manually curated data.
Investigation: 1 hidden item
Study: 1 hidden item
Organisms: No organisms
Models: Data and Jupyter notebooks for our analysis on ..., Data and Jupyter notebooks for our analysis on ...
SOPs: No SOPs
Data files: Schematic overview of sc-SynO, sc-SynO use case 1: cardiac glial cell identifi..., sc-SynO use case 2: proliferative cardiomyocyte...
Here, we conduct a proof of principle by comparing a 2D and 3D fluorescent image analysis based approach on unlabeled cardiomyocytes. Based on the CellProfiler software, we extracted high-dimensional features of individual cells and nuclei, which are subsequently down-sampled and clustered. These clusters are furthermore benchmarked via different machine learning classifiers (e.g., AdaBoost, Gradient Boosting, Random Forest) as the ground truth for our proposed approach.
Investigation: 1 hidden item
Study: 1 hidden item
Organisms: No organisms
Models: No Models
SOPs: No SOPs
Data files: 1 hidden item