Extending case-based reasoning by discovering and using image features in IVF
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Intelligent decision support for protein crystal growth
IBM Systems Journal - Deep computing for the life sciences
Conceptual Modeling: Foundations and Applications
Protein structure prediction with visuospatial analogy
SC'06 Proceedings of the 2006 international conference on Spatial Cognition V: reasoning, action, interaction
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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.