https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2024Kimanius_Heterogeneity 2026-05-01T15:21:02Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2024Kimanius_Heterogeneity&diff=4691&oldid=prev 2024-08-14T06:26:04Z

<p>Created page with &quot;== Citation == Kimanius, Dari / Schwab, Johannes. Confronting heterogeneity in cryogenic electron microscopy data: Innovative strategies and future perspectives with data-driven methods. 2024. Current Opinion in Structural Biology, Vol. 86, p. 102815 == Abstract == The surge in the influx of data from cryogenic electron microscopy (cryo-EM) experiments has intensified the demand for robust algorithms capable of autonomously managing structurally heterogeneous dataset...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> Kimanius, Dari / Schwab, Johannes. Confronting heterogeneity in cryogenic electron microscopy data: Innovative strategies and future perspectives with data-driven methods. 2024. <br /> Current Opinion in Structural Biology, Vol. 86, p. 102815<br /> <br /> == Abstract ==<br /> <br /> The surge in the influx of data from cryogenic electron microscopy<br /> (cryo-EM) experiments has intensified the demand for<br /> robust algorithms capable of autonomously managing structurally<br /> heterogeneous datasets. This presents a wealth of exciting<br /> opportunities from a data science viewpoint, inspiring the<br /> development of numerous innovative, application-specific<br /> methods, many of which leverage contemporary data-driven<br /> techniques. However, addressing the challenges posed by<br /> heterogeneous datasets remains a paramount yet unresolved<br /> issue in the field. Here, we explore the subtleties of this challenge<br /> and the array of strategies devised to confront it. We<br /> pinpoint the shortcomings of existing methodologies and deliberate<br /> on prospective avenues for improvement. Specifically, our<br /> discussion focuses on strategies to mitigate model overfitting<br /> and manage data noise, as well as the effects of constraints,<br /> priors, and invariances on the optimization process.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S0959440X24000423<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

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