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Protein Crystallization for X-ray Crystallography
09:27

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Published on: January 16, 2011

Classification of protein crystallization imagery.

Xiaoqing Zhu1, Shaohua Sun, Marshall Bern

  • 1Department of Electrical Engineering, Stanford University, CA, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
PubMed
Summary

This study introduces an automated method for classifying protein crystallization images. A decision-tree classifier with boosting achieved the best performance in distinguishing successful crystallization trials from failed ones.

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Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering
09:15

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

Published on: August 14, 2018

Area of Science:

  • Computational biology
  • Biophysics
  • Machine learning

Background:

  • Protein crystallization is crucial for structural biology.
  • Accurate classification of crystallization imagery aids in optimizing experimental outcomes.
  • Automated methods can improve efficiency and reproducibility in analyzing crystallization trials.

Purpose of the Study:

  • To investigate and evaluate modern mathematical tools for automatic classification of protein crystallization imagery.
  • To compare the performance of Support Vector Machine (SVM) and decision-tree classifiers for this task.
  • To identify the optimal feature extraction and classification strategy for distinguishing successful from failed crystallization attempts.

Main Methods:

  • Feature extraction using a combination of geometric and texture features.
  • Classification using Support Vector Machine (SVM) and an automatic decision-tree classifier.
  • Evaluation on a dataset of 520 protein crystallization images for binary classification (success vs. failure).

Main Results:

  • The decision-tree classifier, utilizing both geometric and texture features with boosting, demonstrated superior performance.
  • The best achieved rates were 14.6% for false positives and 9.6% for false negatives.
  • This approach effectively separates successful protein crystallization trials from failed attempts.

Conclusions:

  • Automated classification of protein crystallization imagery is feasible and effective.
  • A decision-tree classifier combined with boosting and comprehensive feature sets offers high accuracy.
  • This method has the potential to streamline protein crystallization screening and analysis.