{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T05:44:27Z","timestamp":1791956667746,"version":"4.3.4"},"reference-count":0,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"content-version":"am","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Determining the three-dimensional (3D) structure of proteins is a cornerstone of structural biology, providing essential insights into their underlying biological mechanisms and functional roles. Cryogenic electron microscopy (cryo-EM) single-particle analysis allows researchers to investigate biomolecules at near-atomic resolutions, unraveling the structure of biomolecular complexes. However, reconstructing high-fidelity 3D volumes from extremely noisy projections, particularly under the interference of structural heterogeneity, remains a challenging task. Traditional Fourier-domain methods often suffer from frequency-dependent signal attenuation and uneven information distribution. While recent deep-learning-based frameworks attempt to predict high-frequency components directly in frequency space, they are highly susceptible to noise and often introduce artifacts. State-of-the-art (SOTA) real-space method circumvents Fourier-domain defects, yet remains constrained by the inherent spectral bias of multi-layer perceptrons, as fixed positional encoding fails to sufficiently capture intricate high-frequency details.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose cryoTriplane, a real-space 3D homogeneous reconstruction network that utilizes a hybrid spatial representation to adaptively capture fine structural details. By combining fixed positional encoding for noise-robust structural initialization with structure-aware positional encoding for high-frequency refinement, our method achieves 3D reconstructions that are simultaneously globally consistent and locally fine-grained. Comprehensive qualitative and quantitative evaluations across five simulated and four real-world datasets demonstrate that cryoTriplane significantly outperforms existing methods in resolving fine structural features. Furthermore, validation through downstream atomic modeling underscores cryoTriplane\u2019s ability to recover high-quality density maps. Specifically, our method achieves sequence coverage improvements of 4.6% and 5.5% on the EMPIAR-10005 and EMPIAR-10492 datasets over the SOTA approach, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and Implementation<\/jats:title>\n                    <jats:p>Our code is available at \u00a0https:\/\/github.com\/chengyr1999\/CryoTriplane. The real datasets used in this study are publicly available from EMPIAR.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag754","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T11:36:01Z","timestamp":1791891361000},"source":"Crossref","is-referenced-by-count":0,"title":["CryoTriplane: Triplane-based Neural Radiance Field Representation for High-resolution Cryo-EM 3D Homogeneous Reconstruction"],"prefix":"10.1093","author":[{"given":"Yiran","family":"Cheng","sequence":"first","affiliation":[{"name":"Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University Frontiers Science Center for Nonlinear Expectations (Ministry of Education), Research Center for Mathematics and Interdisciplinary Sciences; , Qingdao 266237,","place":["China"]},{"name":"Ningxia Medical University College of Medical Information and Engineering, , Yinchuan 750004,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bintao","family":"He","sequence":"additional","affiliation":[{"name":"Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University Frontiers Science Center for Nonlinear Expectations (Ministry of Education), Research Center for Mathematics and Interdisciplinary Sciences; , Qingdao 266237,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiong","family":"Zeng","sequence":"additional","affiliation":[{"name":"Shandong University School of Computer Science and Technology, , Qingdao 266237,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fa","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology School of Medical Technology, , Beijing 100190,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renmin","family":"Han","sequence":"additional","affiliation":[{"name":"Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University Frontiers Science Center for Nonlinear Expectations (Ministry of Education), Research Center for Mathematics and Interdisciplinary Sciences; , Qingdao 266237,","place":["China"]},{"name":"Ningxia Medical University College of Medical Information and Engineering, , Yinchuan 750004,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,13]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag754\/71920962\/btag754.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag754\/71920962\/btag754.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T04:46:43Z","timestamp":1791953203000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article\/doi\/10.1093\/bioinformatics\/btag754\/8914810"}},"subtitle":[],"editor":[{"given":"Virginie","family":"Uhlmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,10,13]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag754","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,13]]},"article-number":"btag754"}}