Jelenlegi hely
Tanszékcsoporti szeminárium
Félév:
2014/15 II. félév
Helyszín:
SZAB Székház 217. sz.
Dátum:
2015-03-10
Időpont:
14:00-15:00
Előadó:
Horváth Péter (MTA SZBK)
Cím:
Image analysis and machine learing methods for high-content cancer drug discovery
Absztrakt:
In this talk I will give an overview of the computational steps in the analysis of a single cell-based high-content screening, a novel way in modern drug discovery. First, I will present a new microscopic image correction method designed to eliminate vignetting and uneven background effects which, left uncorrected, corrupt intensity-based measurements. Variational methods for single cell segmentation will be presented. I will than discuss the Advanced Cell Classifier (ACC) (www.cellclassifier.org), a software tool capable of identifying cellular types based on features extracted from the image. It provides an interface for a user to efficiently train machine learning methods to predict various phenotypes. For cases where discrete cell-based decisions are not suitable, we propose a method to use multi-parametric regression to analyze continuous biological phenomena. To improve the learning speed and accuracy, we recently developed an active learning scheme which automatically selects the most informative cell samples. Finally, combining the above methods, I will discuss CL2M (correlative light-light microscopy) a revolutionary technique for single-cell analysis of various tumor types.