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Detection and identification of macromolecular complexes in cryo-electron tomograms using support vector machines.
In: Proceedings - International Symposium on Biomedical Imaging (9th IEEE International Symposium on Biomedical Imaging (ISBI), 2-5 May 2012, Barcelona). Piscataway, NJ: IEEE, 2012. 1373-1376
Detection and identification of macromolecular complexes in cryo-electron tomograms is challenging due to the extremely low signal-to-noise ratio (SNR). While the state-of-the-art method is template matching with a single template, we propose a 3-step supervised learning approach: (i) pre-detection of candidates, (ii) feature calculation, and (iii) final decision using a support vector machine (SVM). We use two types of features for SVM: (i) correlation coefficients from multiple templates, and (ii) rotation invariant features derived from spherical harmonics. Experiments conducted on both simulated and experimental tomograms show that our approach outperforms the state-of-the-art method.
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Publication type Article: Conference contribution
Keywords Cryo-electron tomography; spherical harmonics; support vector machines; template matching
ISSN (print) / ISBN 1945-7928
Conference Title 9th IEEE International Symposium on Biomedical Imaging (ISBI)
Conference Date 2-5 May 2012
Conference Location Barcelona
Proceedings Title Proceedings - International Symposium on Biomedical Imaging
Quellenangaben Pages: 1373-1376
Publishing Place Piscataway, NJ
Institute(s) Institute of Computational Biology (ICB)