Paper Info
Title
Detection and identification of macromolecular complexes in cryo-electron tomograms using support vector machines
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-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.
Year
DOI
Venue
2012
10.1109/ISBI.2012.6235823
ISBI
Keywords
Field
DocType
template matching,supervised learning approach,macromolecular complex identification,cryo-electron tomography,rotation invariant feature,biological techniques,biology computing,molecular biophysics,molecular configurations,macromolecular complex detection,support vector machine,macromolecules,spherical harmonics,cryo-electron tomogram,support vector machines,signal to noise ratio,spherical harmonic,supervised learning,correlation,electron tomography,feature extraction,harmonic analysis
Template matching,Computer vision,Pattern recognition,Computer science,Support vector machine,Signal-to-noise ratio,Spherical harmonics,Supervised learning,Feature extraction,Invariant (mathematics),Artificial intelligence,Template
Conference
ISSN
ISBN
Citations 
1945-7928
978-1-4577-1857-1
1
PageRank 
References 
Authors
0.43
0
7
Name
Order
Citations
PageRank
Yuxiang Chen1348.25
Thomas Hrabe2202.17
Stefan Pfeffer321.51
Olivier Pauly415413.13
Diana Mateus541732.74
Nassir Navab66594578.60
Friedrich Forster710.77