https://3demmethods.i2pc.es/index.php?action=history&feed=atom&title=2025Zhan_AITom 2026-05-24T21:06:50Z Revision history for this page on the wiki MediaWiki 1.44.2 https://3demmethods.i2pc.es/index.php?title=2025Zhan_AITom&diff=5003&oldid=prev 2025-06-24T06:24:03Z

<p>Created page with &quot;== Citation == X. Zhan, X. Zeng, M. R. Uddin, and M. Xu, “AITom: AI-guided cryo-electron tomography image analyses toolkit,” Journal of Structural Biology, p. 108207, 2025. == Abstract == Cryo-electron tomography (cryo-ET) is an essential tool in structural biology, uniquely capable of visualizing three-dimensional macromolecular complexes within their native cellular environments, thereby providing profound molecular-level insights. Despite its significant promis...&quot;</p> <p><b>New page</b></p><div>== Citation ==<br /> <br /> X. Zhan, X. Zeng, M. R. Uddin, and M. Xu, “AITom: AI-guided cryo-electron tomography image analyses toolkit,” Journal of Structural Biology, p. 108207, 2025.<br /> <br /> == Abstract ==<br /> <br /> Cryo-electron tomography (cryo-ET) is an essential tool in structural biology, uniquely capable of visualizing<br /> three-dimensional macromolecular complexes within their native cellular environments, thereby providing<br /> profound molecular-level insights. Despite its significant promise, cryo-ET faces persistent challenges in the<br /> systematic localization, identification, segmentation, and structural recovery of three-dimensional subcellular<br /> components, necessitating the development of efficient and accurate large-scale image analysis methods. In<br /> response to these complexities, this paper introduces AITom, an open-source artificial intelligence platform<br /> tailored for cryo-ET researchers. AITom integrates a comprehensive suite of public and proprietary algorithms,<br /> supporting both traditional template-based and template-free approaches, alongside state-of-the-art deep<br /> learning methodologies for cryo-ET data analysis. By incorporating diverse computational strategies, AITom<br /> enables researchers to more effectively tackle the complexities inherent in cryo-ET, facilitating precise analysis<br /> and interpretation of complex biological structures. Furthermore, AITom provides extensive tutorials for each<br /> analysis module, offering valuable guidance to users in utilizing its comprehensive functionalities.<br /> <br /> == Keywords ==<br /> <br /> == Links ==<br /> <br /> https://www.sciencedirect.com/science/article/pii/S1047847725000425<br /> <br /> == Related software ==<br /> <br /> == Related methods ==<br /> <br /> == Comments ==</div>

WikiSysop