期刊
BIOINFORMATICS
卷 29, 期 19, 页码 2460-2468出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btt429
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资金
- Spanish Ministry of Economy and Competitiveness [AIC-A-2011-0638, BFU2009-09331, BIO2010-16566, CAM(S2010/BMD-2305)]
- NSF [1114901]
- postdoctoral Juan de la Cierva Grants [JCI-2011-10185, JCI-2010-07594]
- la Caixa scholarship
- Formacion de Personal Investigador (FPI) fellowship
- CSIC grant (JAE-Predoc)
- Ramon y Cajal fellowship
- Israel Science Foundation [485/10]
- Instruct, part of the European Strategy Forum on Research Infrastructures (ESFRI)
- national member subscriptions
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1114901] Funding Source: National Science Foundation
Motivation: Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. Achieving high-resolution structural details with electron microscopy requires the identification of a large number (up to hundreds of thousands) of single particles from electron micrographs, which is a laborious task if it has to be manually done and constitutes a hurdle towards high-throughput. Automatic particle selection in micrographs is far from being settled and new and more robust algorithms are required to reduce the number of false positives and false negatives. Results: In this article, we introduce an automatic particle picker that learns from the user the kind of particles he is interested in. Particle candidates are quickly and robustly classified as particles or non-particles. A number of new discriminative shape-related features as well as some statistical description of the image grey intensities are used to train two support vector machine classifiers. Experimental results demonstrate that the proposed method: (i) has a considerably low computational complexity and (ii) provides results better or comparable with previously reported methods at a fraction of their computing time.
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