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		<title>WikiSysop: Created page with &quot;== Citation ==  Gilles, Marc Aurèle / Singer, Amit. A molecular prior distribution for Bayesian inference based on Wilson statistics. 2022. Computer Methods and Programs in B...&quot;</title>
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		<updated>2023-01-10T09:03:11Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;== Citation ==  Gilles, Marc Aurèle / Singer, Amit. A molecular prior distribution for Bayesian inference based on Wilson statistics. 2022. Computer Methods and Programs in B...&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;
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Gilles, Marc Aurèle / Singer, Amit. A molecular prior distribution for Bayesian inference based on Wilson statistics. 2022. Computer Methods and Programs in Biomedicine, Vol. 221 &lt;br /&gt;
p. 106830&lt;br /&gt;
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== Abstract ==&lt;br /&gt;
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Background and Objective: Wilson statistics describe well the power spectrum of proteins at high frequen- cies. Therefore, it has found several applications in structural biology, e.g., it is the basis for sharpening steps used in cryogenic electron microscopy (cryo-EM). A recent paper gave the first rigorous proof of Wilson statistics based on a formalism of Wilson’s original argument. This new analysis also leads to statistical estimates of the scattering potential of proteins that reveal a correlation between neighboring Fourier coefficients. Here we exploit these estimates to craft a novel prior that can be used for Bayesian inference of molecular structures. Methods: We describe the properties of the prior and the computation of its hyperparameters. We then evaluate the prior on two synthetic linear inverse problems, and compare against a popular prior in cryo- EM reconstruction at a range of SNRs. Results: We show that the new prior effectively suppresses noise and fills-in low SNR regions in the spectral domain. Furthermore, it improves the resolution of estimates on the problems considered for a wide range of SNR and produces Fourier Shell Correlation curves that are insensitive to masking effects. Conclusions: We analyze the assumptions in the model, discuss relations to other regularization strategies, and postulate on potential implications for structure determination in cryo-EM.&lt;br /&gt;
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== Keywords ==&lt;br /&gt;
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== Links ==&lt;br /&gt;
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https://www.sciencedirect.com/science/article/pii/S0169260722002127&lt;br /&gt;
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== Related software ==&lt;br /&gt;
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== Related methods ==&lt;br /&gt;
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== Comments ==&lt;/div&gt;</summary>
		<author><name>WikiSysop</name></author>
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