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SELECTBIO Conferences High-Content and Phenotypic Screening Europe 2018


Phenotypic Discovery Using Machine Learning and Image Analysis Methods

Peter Horvath, Finnish Distinguished Professor, Fellow FIMM Helsinki and Group Leader, Hungarian Academia of Sciences

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 the Advanced Cell Classifier (ACC) (, a software tool capable of identifying cellular phenotypes based on features extracted from the image. It provides an interface for a user to efficiently train machine learning methods to predict various phenotypes. We developed the Suggest a Learner (SALT) toolbox, which selects the optimal machine learning algorithm and parameters for a particular classification problem. 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, to improve the learning speed and accuracy, we recently developed an active learning scheme which automatically selects the most informative cell samples.

Add to Calendar ▼2018-05-24 00:00:002018-05-25 00:00:00Europe/LondonHigh-Content and Phenotypic Screening Europe 2018High-Content and Phenotypic Screening Europe 2018 in Cambridge, UKCambridge,