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A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy

Overview of attention for article published in BMC Bioinformatics, July 2017
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  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

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12 tweeters

Citations

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57 Dimensions

Readers on

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112 Mendeley
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Title
A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy
Published in
BMC Bioinformatics, July 2017
DOI 10.1186/s12859-017-1757-y
Pubmed ID
Authors

Yanan Zhu, Qi Ouyang, Youdong Mao

Abstract

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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 12 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 112 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 29%
Researcher 24 21%
Student > Bachelor 10 9%
Student > Master 9 8%
Student > Doctoral Student 5 4%
Other 16 14%
Unknown 15 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 15%
Engineering 16 14%
Computer Science 14 13%
Agricultural and Biological Sciences 12 11%
Physics and Astronomy 10 9%
Other 24 21%
Unknown 19 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 June 2020.
All research outputs
#4,298,685
of 16,301,805 outputs
Outputs from BMC Bioinformatics
#1,779
of 5,892 outputs
Outputs of similar age
#74,493
of 268,952 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 23 outputs
Altmetric has tracked 16,301,805 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 5,892 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 69% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 268,952 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.