GET THE APP

..

Journal of Computer Science & Systems Biology

ISSN: 0974-7230

Open Access

Data Mining Techniques in High Content Screening: A Survey

Abstract

Karol Kozak, Aagya Agrawal, Nikolaus Machuy and Gabor Csucs

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.

PDF

Share this article

Google Scholar citation report
Citations: 2279

Journal of Computer Science & Systems Biology received 2279 citations as per Google Scholar report

Journal of Computer Science & Systems Biology peer review process verified at publons

Indexed In

 
arrow_upward arrow_upward