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Detection and identification of macromolecular complexes in cryo-electron tomograms using support vector machines

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Chen,  Yuxiang
Baumeister, Wolfgang / Molecular Structural Biology, Max Planck Institute of Biochemistry, Max Planck Society;

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Hrabe,  Thomas
Baumeister, Wolfgang / Molecular Structural Biology, Max Planck Institute of Biochemistry, Max Planck Society;

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Pfeffer,  Stefan
Baumeister, Wolfgang / Molecular Structural Biology, Max Planck Institute of Biochemistry, Max Planck Society;

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Förster,  Friedrich
Baumeister, Wolfgang / Molecular Structural Biology, Max Planck Institute of Biochemistry, Max Planck Society;

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Citation

Chen, Y., Hrabe, T., Pfeffer, S., Pauly, O., Mateus, D., Navab, N., et al. (2012). Detection and identification of macromolecular complexes in cryo-electron tomograms using support vector machines. In Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on (pp. 1373-1376). IEEE Xplore.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000E-BB7D-8
Abstract
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-ofthe- art method is template matching with a single template, we propose a 3-step supervised learning approach: (i) predetection 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.