Speaker
Peter Horvath
Description
In this talk I will give an overview of the computational steps in the analysis of a single cell-based high-content screen. First, I will present a novel microscopic image correction method designed to eliminate vignetting and uneven background effects which, left uncorrected, corrupt intensity-based measurements. I will discuss methods capable of identifying cellular phenotypes based on features extracted from the image using deep learning and other advanced machine learning algorithms. For cases where discrete cell-based decisions are not suitable, we propose a method to use multi-parametric regression to analyze continuous biological phenomena. Finally single cell selection and isolation methods using machine learning will be discussed.
