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	<title>2025Chen CryoCRAB - Revision history</title>
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	<updated>2026-05-24T21:10:58Z</updated>
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		<id>https://3demmethods.i2pc.es/index.php?title=2025Chen_CryoCRAB&amp;diff=5023&amp;oldid=prev</id>
		<title>WikiSysop: Created page with &quot;== Citation ==  Q. Chen et al., “A large-scale curated and filterable dataset for cryo-EM foundation model pre-training,” Scientific Data, vol. 12, no. 1, p. 960, 2025.  == Abstract ==  Cryo-electron microscopy (cryo-EM) is a transformative imaging technology that enables near-atomic resolution 3D reconstruction of target biomolecule, playing a critical role in structural biology and drug discovery. Cryo-EM faces significant challenges due to its extremely low signal...&quot;</title>
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		<updated>2025-07-10T06:34:03Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;== Citation ==  Q. Chen et al., “A large-scale curated and filterable dataset for cryo-EM foundation model pre-training,” Scientific Data, vol. 12, no. 1, p. 960, 2025.  == Abstract ==  Cryo-electron microscopy (cryo-EM) is a transformative imaging technology that enables near-atomic resolution 3D reconstruction of target biomolecule, playing a critical role in structural biology and drug discovery. Cryo-EM faces significant challenges due to its extremely low signal...&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;
Q. Chen et al., “A large-scale curated and filterable dataset for cryo-EM foundation model pre-training,” Scientific Data, vol. 12, no. 1, p. 960, 2025.&lt;br /&gt;
&lt;br /&gt;
== Abstract ==&lt;br /&gt;
&lt;br /&gt;
Cryo-electron microscopy (cryo-EM) is a transformative imaging technology that enables near-atomic&lt;br /&gt;
resolution 3D reconstruction of target biomolecule, playing a critical role in structural biology and drug&lt;br /&gt;
discovery. Cryo-EM faces significant challenges due to its extremely low signal-to-noise ratio (SNR)&lt;br /&gt;
where the complexity of data processing becomes particularly pronounced. To address this challenge,&lt;br /&gt;
foundation models have shown great potential in other biological imaging domains. However, their&lt;br /&gt;
application in cryo-EM has been limited by the lack of large-scale, high-quality datasets. To fill this&lt;br /&gt;
gap, we introduce CryoCRAB, the first large-scale dataset for cryo-EM foundation models. CryoCRAB&lt;br /&gt;
includes 746 proteins, comprising 152,385 sets of raw movie frames (116.8 TB in total). To tackle the&lt;br /&gt;
high-noise nature of cryo-EM data, each movie is split into odd and even frames to generate paired&lt;br /&gt;
micrographs for denoising tasks. The dataset is stored in HDF5 chunked format, significantly improving&lt;br /&gt;
random sampling efficiency and training speed. CryoCRAB offers diverse data support for cryo-EM&lt;br /&gt;
foundation models, enabling advancements in image denoising and general-purpose feature extraction&lt;br /&gt;
for downstream tasks.&lt;br /&gt;
&lt;br /&gt;
== Keywords ==&lt;br /&gt;
&lt;br /&gt;
== Links ==&lt;br /&gt;
&lt;br /&gt;
https://www.nature.com/articles/s41597-025-05179-2&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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