Improving Objectivity and Scalability in Protein Crystallization: Integrating Image Analysis With Knowledge Discovery

  • Authors:
  • Igor Jurisica;Patrick Rogers;Janice I. Glasgow;Robert J. Collins;Jennifer R. Wolfley;Joseph R. Luft;Goerge T. DeTitta

  • Affiliations:
  • -;-;-;-;-;-;-

  • Venue:
  • IEEE Intelligent Systems
  • Year:
  • 2001

Quantified Score

Hi-index 0.00

Visualization

Abstract

This article describes issues related to integrating image analysis techniques with knowledge discovery and case-based reasoning. Although the work applies to many problem domains, here we focus on analyzing and classifying outcomes of protein crystallization experiments in high-throughput structural genomics. We apply the fast Fourier transform to analyze image content to extract important features of the spectrum. We use a combination of these features to classify crystallization experiments' outcomes. Although humans can analyze images more flexibly, a computational approach makes the process scalable and more objective. We evaluate the classification process and present results on how we can combine automatically extracted features to discover important crystallographic knowledge.