Expertise: Bioinformatics, Computational Systems Biology, Data Management, Databases, Python, R, Transcriptomics, Image analysis, Genomics, Molecular Biology, Microbiology, Data analysis, Genetics
Tools: qPCR, Isolation purification and separation, Genomics, Data Science, RNA / DNA Techniques, Transcriptomics, Microbiology, Molecular Biology, Databases
I am a biochemist & bio-informatician working in phytobacteriology at the Plant Sciences Unit of ILVO, the Flanders Research Institute for Agricultural, Fisheries and Food Research. The focus is on genomics-based research and diagnostics for Plant Health.
My expertise is 'wet-lab' work (microbiology, sequencing, molecular biology, design & validation of diagnostics assays using qPCR/LAMP, automatisation) and 'dry-lab' work such as bio-informatics/data analysis (e.g. scripting analysis ...
Projects: Not specified
Institutions: Not specified
Expertise: Cell biology, DNA repair, Cancer Biology, Cancer research, Molecular Biology, Curation, Cell physiology, Data analysis, Visualization, Graphical Editors, Image analysis
Tools: Animal models, Cell and tissue culture, In vivo bioluminescence imaging, Molecular biology techniques (RNA/DNA/Protein), Cytometry and fluorescent microscopy, Adobe Illustrator, GraphPad Prism, Adobe Photoshop
M.Sc. graduate specializing in medical science, translational cancer research, cell biology, and DNA damage repair.
Expertise: Biochemistry, Cell biology, Data analysis, Dynamic modelling, Systems Biology, Image analysis, Genetics, Molecular Biology, R, SBML, Curation, Quantitative Biology, Physical Chemistry
Tools: Biochemistry and protein analysis, Bioinformatics, Systems Biology, SBML, R, ODE, Molecular biology techniques (RNA/DNA/Protein), Genetics, Dynamic modelling, Computational and theoretical biology, CellDesigner, Parameter estimation
Projects: iRhythmics
Institutions: Rostock University Medical Centre
Projects: Not specified
Institutions: Not specified
Projects: ICYSB 2015 - International Practical Course in Systems Biology
Institutions: VU University Amsterdam
Expertise: Single Cell analysis, Image analysis
I am a beginning PhD student at the VU in Amsterdam and study the heterogeneity of yeast cells at near zero growth conditions. I have a versatile background in Biophysics and Systems Biology.
Projects: ICYSB 2015 - International Practical Course in Systems Biology
Institutions: University of Tokyo
Expertise: Genetics, Genomics, Cell biology, Image analysis, Phenome, Genetic interaction
Tools: Fluorescence microscopy, CalMorph, R, Java, GLM, Model selection
I am a postdoc in The University of Tokyo. My research interest is to know structure of biological systems by functional relationships between genes working in response to endogenous and/or exogenous disruptions. I'm trying to find functional relationships between genes by similarity of phenotype defined with high-dimensional morphological features in yeast gene deletion collection. I'm happy if you will talk with me in any topics.
Projects: SulfoSys - Biotec, ICYSB 2015 - International Practical Course in Systems Biology
Institutions: Otto-von-Guericke University Magdeburg, University of Gothenburg
https://orcid.org/0000-0001-6971-2530Expertise: Signalling networks, dynamics of biological networks., Databases, Data analysis, Systems Biology, Model selection, Identifiability, Cellular Senescence, Cell Cycle, Dynamic Systems, Image processing, Image analysis, Parameter estimation
Tools: ODE, FACS, Model selection, Fluorescence and confocal microscopy, Identifiability analysis, Parameter estimation
My group investigates dynamic regulation and control mechanisms of cellular signal transduction networks by a combination of theoretical, experimental and computational methods. We seek to make sense of our biological data with the help of mathematical models, which ideally enable us to make valid predictions for new experiments, thereby generating novel biological insights.
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.
Submitter: Markus Wolfien
Biological problem addressed: Model Analysis Type
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