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Making Protein Localization Features More Robust

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.

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Making Protein Localization Features More Robust

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

  2. 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

  3. 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.

  4. Online Image Classification Results Overall accuracy = 18%

  5. Without Texture Features Overall accuracy = 34%

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

  7. 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

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