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	<title>2024Feng DeepQs - Revision history</title>
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	<updated>2026-05-24T22:01:20Z</updated>
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		<id>https://3demmethods.i2pc.es/index.php?title=2024Feng_DeepQs&amp;diff=4582&amp;oldid=prev</id>
		<title>WikiSysop: Created page with &quot;== Citation ==  Feng, Ming-Feng / Chen, Yu-Xuan / Shen, Hong-Bin. DeepQs: Local quality assessment of cryo-EM density map by deep learning map-model fit score. 2024. J. Structural Biology, Vol. 216, No. 1, p. 108059  == Abstract ==  Cryogenic electron microscopy maps are valuable for determining macromolecule structures. A proper quality assessment method is essential for cryo-EM map selection or revision. This article presents DeepQs, a novel approach to estimate local...&quot;</title>
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		<updated>2024-07-31T06:11:51Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;== Citation ==  Feng, Ming-Feng / Chen, Yu-Xuan / Shen, Hong-Bin. DeepQs: Local quality assessment of cryo-EM density map by deep learning map-model fit score. 2024. J. Structural Biology, Vol. 216, No. 1, p. 108059  == Abstract ==  Cryogenic electron microscopy maps are valuable for determining macromolecule structures. A proper quality assessment method is essential for cryo-EM map selection or revision. This article presents DeepQs, a novel approach to estimate local...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;== Citation ==&lt;br /&gt;
&lt;br /&gt;
Feng, Ming-Feng / Chen, Yu-Xuan / Shen, Hong-Bin. DeepQs: Local quality assessment of cryo-EM density map by deep learning map-model fit score. 2024. J. Structural Biology, Vol. 216, No. 1, p. 108059&lt;br /&gt;
&lt;br /&gt;
== Abstract ==&lt;br /&gt;
&lt;br /&gt;
Cryogenic electron microscopy maps are valuable for determining macromolecule structures. A proper quality&lt;br /&gt;
assessment method is essential for cryo-EM map selection or revision. This article presents DeepQs, a novel&lt;br /&gt;
approach to estimate local quality for 3D cryo-EM density maps, using a deep-learning algorithm based on mapmodel&lt;br /&gt;
fit score. DeepQs is a parameter-free method for users and incorporates structural information between&lt;br /&gt;
map and its related atomic model into well-trained models by deep learning. More specifically, the DeepQs&lt;br /&gt;
approach leverages the interplay between map and atomic model through predefined map-model fit score, Qscore.&lt;br /&gt;
DeepQs can get close results to the ground truth map-model fit scores with only cryo-EM map as input. In&lt;br /&gt;
experiments, DeepQs demonstrates the lowest root mean square error with standard method Fourier shell correlation&lt;br /&gt;
metric and high correlation with map-model fit score, Q-score, when compared with other local quality&lt;br /&gt;
estimation methods in high-resolution dataset (&amp;lt;=5 Å). DeepQs can also be applied to evaluate the quality of the&lt;br /&gt;
post-processed maps. In both cases, DeepQs runs faster by using GPU acceleration. Our program is available at htt&lt;br /&gt;
p://www.csbio.sjtu.edu.cn/bioinf/DeepQs for academic use.&lt;br /&gt;
&lt;br /&gt;
== Keywords ==&lt;br /&gt;
&lt;br /&gt;
== Links ==&lt;br /&gt;
&lt;br /&gt;
https://www.sciencedirect.com/science/article/pii/S1047847723001223&lt;br /&gt;
&lt;br /&gt;
== Related software ==&lt;br /&gt;
&lt;br /&gt;
== Related methods ==&lt;br /&gt;
&lt;br /&gt;
== Comments ==&lt;/div&gt;</summary>
		<author><name>WikiSysop</name></author>
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