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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2016.2627573
|2 doi
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|a eng
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| 100 |
1 |
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|a Punjani, Ali
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Building Proteins in a Day
|b Efficient 3D Molecular Structure Estimation with Electron Cryomicroscopy
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| 264 |
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|c 2017
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|a Text
|b txt
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|a ƒaComputermedien
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|a Date Completed 04.10.2018
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|a Date Revised 04.10.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Discovering the 3D atomic-resolution structure of molecules such as proteins and viruses is one of the foremost research problems in biology and medicine. Electron Cryomicroscopy (cryo-EM) is a promising vision-based technique for structure estimation which attempts to reconstruct 3D atomic structures from a large set of 2D transmission electron microscope images. This paper presents a new Bayesian framework for cryo-EM structure estimation that builds on modern stochastic optimization techniques to allow one to scale to very large datasets. We also introduce a novel Monte-Carlo technique that reduces the cost of evaluating the objective function during optimization by over five orders of magnitude. The net result is an approach capable of estimating 3D molecular structure from large-scale datasets in about a day on a single CPU workstation
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Brubaker, Marcus A
|e verfasserin
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| 700 |
1 |
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|a Fleet, David J
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 39(2017), 4 vom: 16. Apr., Seite 706-718
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|x 1939-3539
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