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== Citation ==
Zhu, Y., Ouyang, Q., & Mao, Y. (2017). A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy. BMC bioinformatics, 18(1), 348.

== Abstract ==
Background: Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural
determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often
requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus
can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing
computational methods for particle picking often use low-resolution templates for particle matching, making
them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for
the automatic recognition of particle images from cryo-EM micrographs.

Results: We developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from
noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion.
The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be
recursively trained to be highly “knowledgeable”. Our approach exhibits an improved performance and accuracy
when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM
datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true
particles contain fewer features.

Conclusions: The DeepEM methodology, derived from a deep CNN, allows automated particle extraction from
raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity
and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification,
significantly improving the efficiency of cryo-EM data processing.

== Keywords ==
Cryo-EM, Particle recognition, Convolutional neural network, Deep learning, Single-particle
reconstruction

== Links ==
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1757-y

== Related software ==

== Related methods ==

== Comments ==

2017Zhu DeepEM - Revision history

Tmajtner: Created page with "== Citation == Zhu, Y., Ouyang, Q., & Mao, Y. (2017). A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy. BMC bioinformati..."