Data Mining Techniques in High Content Screening: A Survey
- *Corresponding Author:
- Dr. Karol Kozak
E-mail : [email protected]
Received date: June 10, 2009; Accepted date: July 12, 2009; Published date: July 12, 2009
Citation: Kozak K, Agrawal A, Machuy N, Csucs G (2009) Data Mining Techniques in High Content Screening: A Survey. J Comput Sci Syst Biol 2:219-239. doi:10.4172/jcsb.1000035
Copyright: © 2009 Kozak K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Advanced microscopy and corresponding image analysis have evolved in recent years as a compelling tool for studying molecular and morphological events in cells and tissues. Cell-based High-Content Screening (HCS) is an upcoming technique for the investigation of cellular processes and their alteration by multiple chemical or genetic perturbations. The analysis of the large amount of data generated in HCS experiments represents a significant challenge and is currently a bottleneck in many screening projects. This article reviews the different ways to analyse large sets of HCS data, including the questions that can be asked and the challenges in interpreting the measurements. The main data mining approaches used in HCS are image descriptors, computations, normalization, quality control methods and classification algorithms.