Medical Journals

Characteristics and Value of Directed Algorithms in High Content Screening.

Authors:
  • Ghosh Richik N
  • Lapets Oleg
  • Haskins Jeffrey R

From: Cellomics, Inc., Pittsburgh, PAUSA.

Methods in molecular biology (Clifton, N.J.)

  • Publish Date: 2007
  • ISSN: 1064-3745
  • Volume: 356
  • Issue:
  • Pages: 63-81
  • Medium: Print
  • Language: English
  • Citation (JAMA): Ghosh Richik N, Lapets Oleg, Haskins Jeffrey R, et al. Characteristics and Value of Directed Algorithms in High Content Screening.. Methods Mol. Biol. 2007;356:63-81

Abstract

High content screening requires image processing algorithms that can accurately and robustly analyze large image numbers without requiring human intervention. Thus, a suite of algorithms that are directed by an understanding of the biology being studied was developed for the optimized automated acquisition and quantitation of cellular images. Two categories of directed algorithms were developed: Developer Tools for assay development and Specific Algorithms for turnkey screening of specific biological situations. The same basic sequence of analysis steps are used in these directed algorithms: 1. Primary object identification. 2. Measurement of primary object properties. 3. Identification and measurements of associated targets. 4. Analysis of raw measurements for specific biological problems. The detailed application of these steps is guided by the biology being studied and the expected phenotypic changes. Most cell biological problems to be analyzed using high content screening can be categorized by either the phenotype of the problem or labeling pattern, or by a standard biological response behavior of the cells. This enables application of directed algorithms optimized for these categories. Examples of the use of directed algorithms for specific categories are discussed, as well as the detailed analysis steps for a specific directed algorithm.

Mesh Headings (Keywords): Algorithms, Animals, Image Processing, Computer-Assisted, Software, Tissue Array Analysis


Check for Full Text / PubMed Unique Identifier (PMID): 16988395


This abstract is part of PubMed, a service of the U.S. National Library of Medicine. PubMed includes more than 17 million citations from MEDLINE and other life science journals for biomedical articles. See Copyright and Disclaimers.

Linked medical terms appearing on this page are added by Healia to help readers find more information and are not part of the original PubMed document.

The data herein was last updated on July 8th, 2008 and may not reflect the most current and accurate data available from NLM.


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