Making Protein Localization Features More Robust Meel Velliste Carnegie Mellon University.

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Image Interpretation Methods for Protein Location in Cells Meel Velliste Murphy Lab Dept. of Biomedical Engineering Carnegie Mellon University Copyright.

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Presentation transcript:

Making Protein Localization Features More Robust Meel Velliste Carnegie Mellon University

Introduction 2-D features work well on our own image set Can they be put to practical use? The features need to work on other people’s images

Images from online journals Fig. 8. Expression of a GMAP-210 mutant form lacking the microtubule-binding site. After transfection, cells were fixed and double labeled for GMAP-210 (a) and the medial Golgi marker CTR433 (b). Red and green image pair is shown in c. Arrow in b indicates a nontransfected cell. Alternatively, cells were stained for GMAP-210 (d, e) and alpha-tubulin (f, g) and image pairs are shown in h and i, respectively. In d, f, and h, a transfected cell is shown. In e, g, and i, a nontransfected cell is presented for comparison. Bars, 10 µm.

Online Image Classification Results Overall accuracy = 18%

Without Texture Features Overall accuracy = 34%

Other Sensitive Features Removed Overall accuracy = 45% (c.f. 68% for our images)

Conclusions System can be used to find Golgi or Tubulin images Some robustness can be achieved by eliminating features sensitive to imaging technique Need additional robust features to improve performance Need a more systematic approach to selecting robust features