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Classification of Protein Localization Patterns in 3-D

Classification of Protein Localization Patterns in 3-D. Meel Velliste Carnegie Mellon University. Introduction. Need a Systematics for Protein Localization Need Microscope Automation Feature based classification of Localization Patterns Pioneering work done with 2D images

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Classification of Protein Localization Patterns in 3-D

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  1. Classification of Protein Localization Patterns in 3-D Meel Velliste Carnegie Mellon University

  2. Introduction • Need a Systematics for Protein Localization • Need Microscope Automation • Feature based classification of Localization Patterns • Pioneering work done with 2D images • Now exploring classification of 3D images

  3. Ten Major Classes of Protein Localization

  4. Features • Derive Numeric Features based on: • Morphology • Texture • Moments feature1 feature2 ... featureN Image1 0.3489 0.1294 ... 1.9012 Image2 0.4985 0.4823 ... 1.8390 ... ... ImageM 1.8245 0.8290 ... 0.9018

  5. Classification • Tried: • Classification Trees • kNN • BPNN • BPNN was the most successful with 84% correct classification rate This is a cyto-skeletal protein

  6. Results of 2-D Classification Overall accuracy = 84%

  7. Motivation for 3-D Classification • Cells are 3-dimensional objects • 2-D images take a slice through the cell • Resultant images are largely dependent on the z-position of the slice • Losing a lot of 3-D structural information

  8. The Approach • Acquire a set of 3-D images for the same 10 classes as used in the 2-D work (have 5 now) • Calculate equivalent features to what was used with the 2-D images • Compare performance

  9. 3-D Classification • Used a subset of the same Morphological features as used with 2-D patterns: • Number of Objects • Euler Number • Average Object Size • Standard Deviation of Object sizes • Ratio of the Largest to the Smallest Object Size • Average Distance of Objects from COF • Standard Deviation of Object Distances from COF • Ratio of the Largest to Smallest Object Distance

  10. 3-D Classification Results Overall accuracy = 84% (95% with GPP=Giantin)

  11. 2-D Results — Same 8 Features Overall accuracy = 84% (95% with GPP=Giantin)

  12. Conclusion • Further work needed to determine if there is any advantage to using 3D images over 2D images • Need to design new features to take advantage of extra information in 3D images

  13. Acknowledgements • Elizabeth Wu - acquired the 3-D image set • Michael V. Boland & Robert F. Murphy - pioneering work on 2-D images

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