Automated classification of images from crystallisation experiments

  • Authors:

  • Julie Wilson

  • Affiliations:

  • York Structural Biology Laboratory, Department of Chemistry, University of York, Heslington, York, UK

  • Venue:
  • ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
  • Year:
  • 2006

  • Local Modelling in Classification

    ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects

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Abstract

Protein crystallography can often provide the three-dimensional structures of macro-molecules necessary for functional studies and drug design. However, identifying the conditions that will provide diffraction quality crystals often requires numerous experiments. The use of robots has led to a dramatic increase in the number of crystallisation experiments performed in most laboratories and, in structural genomics centres, tens of thousands of experiments can be produced daily. The results of these experiments must be assessed repeatedly over time and inspection of the results by eye is becoming increasingly impractical. A number of systems are now available for automated imaging of crystallisation experiments and the primary aim of this research is the development of software to automate image analysis.