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Improving Objectivity and Scalability in Protein Crystallization: Integrating Image Analysis With Knowledge Discovery
November/December 2001 (vol. 16 no. 6)
pp. 26-34

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.

Citation:
Igor Jurisica, Patrick Rogers, Janice I. Glasgow, Robert J. Collins, Jennifer R. Wolfley, Joseph R. Luft, Goerge T. DeTitta, "Improving Objectivity and Scalability in Protein Crystallization: Integrating Image Analysis With Knowledge Discovery," IEEE Intelligent Systems, vol. 16, no. 6, pp. 26-34, Nov.-Dec. 2001, doi:10.1109/5254.972075
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