https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2025Farheen_Modeling 2026-05-24T21:07:15Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2025Farheen_Modeling&diff=4950&oldid=prev 2025-03-04T11:51:56Z

<p>Created page with &quot;== Citation == F. Farheen, G. Terashi, H. Zhu, and D. Kihara, “AI-based methods for biomolecular structure modeling for Cryo-EM,” Current Opinion in Structural Biology, vol. 90, p. 102989, 2025. == Abstract == Cryo-electronmicroscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of macromolecular structures that were challenging to study with conventional methods. Processing cryo-EM data involves several computational steps to deriv...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> F. Farheen, G. Terashi, H. Zhu, and D. Kihara, “AI-based methods for biomolecular structure modeling for Cryo-EM,” Current Opinion in Structural Biology, vol. 90, p. 102989, 2025.<br /> <br /> == Abstract ==<br /> <br /> Cryo-electronmicroscopy (Cryo-EM) has revolutionized structural<br /> biology by enabling the determination of macromolecular structures<br /> that were challenging to study with conventional methods.<br /> Processing cryo-EM data involves several computational steps to<br /> derive three-dimensional structures from raw projections. Recent<br /> advancements in artificial intelligence (AI) including deep learning<br /> have significantly improved the performance of these processes.<br /> In this review, we discuss state-of-the-art AI-based techniques<br /> used in key steps of cryo-EM data processing, including macromolecular<br /> structure modeling and heterogeneity analysis.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S0959440X25000077<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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