Institute Director and Group Leader, Institute of Biochemistry, Biological Research Centre, Szeged and Research Fellow at the Institute for Molecular Medicine Finland (FIMM), Helsinki
In this webinar, you will discover:
This webinar provides an overview of the computational steps in the analysis of single cell-based large-scale microscopy experiments. First, you’ll learn about a novel microscopic image correction method designed to eliminate illumination and uneven background effects, which, left uncorrected, corrupt intensity-based measurements. New single-cell image segmentation methods will be presented using differential geometry, energy minimization, and deep learning methods (www.nucleAIzer.org).
Second, we’ll introduce the Advanced Cell Classifier (ACC) (www.cellclassifier.org), a machine learning software tool capable of identifying cellular phenotypes based on features extracted from the image. It provides an interface for users to efficiently train machine learning methods to predict various phenotypes. For cases where discrete cell-based decisions are not suitable, we suggest using multi-parametric regression to analyze continuous biological phenomena. To improve learning speed and accuracy, we propose an active learning scheme that selects the most informative cell samples.
Finally, you will see how the above machine learning models were used to develop single-cell isolation methods based on laser-microcapturing and patch clamping. You will also discover how DNA and RNA sequencing, proteomics, lipidomics, and targeted electrophysiology measurements were successfully performed on isolated cells.
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