{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T11:49:58Z","timestamp":1782301798134,"version":"3.54.5"},"reference-count":91,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:00:00Z","timestamp":1767139200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Biology"],"published-print":{"date-parts":[[2026,1,1]]},"abstract":"<jats:p>\n                    The intricate three-dimensional structures of proteins dictate their diverse biological functions. Accurately representing these structures, particularly the fine-grained orientational relationships between amino acids, is crucial for understanding protein mechanisms and practical computational protein analysis. Current methodologies often fall short in capturing these essential geometric details. We introduce Orientation-Aware Graph Neural Networks (OA-GNNs), a novel deep learning framework that explicitly models local and global geometric characteristics, including inner-residue torsion angles and inter-residue orientations. OA-GNNs achieve this by uniquely extending neural network weights from scalars to 3D directed weights and by implementing an equivariant message passing paradigm that ensures SO(3)-equivariance. This approach allows for a richer, more geometrically meaningful processing of protein structural data. Comprehensive experiments demonstrate that OA-GNNs significantly outperform existing methods in sensing orientational features and achieve state-of-the-art performance across diverse computational biology tasks, including residue identification, computational protein design, model quality assessment, and protein function classification. Our findings highlight the power of orientation-aware learning and establish OA-GNNs as a versatile and robust tool for advancing our understanding of protein structure\u2013function relationships and for developing new therapeutic and biotechnological solutions. The code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Ced3-han\/OAGNN\/tree\/main\">https:\/\/github.com\/Ced3-han\/OAGNN\/tree\/main<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1177\/15578666251406300","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T16:55:29Z","timestamp":1767113729000},"page":"90-106","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning Protein Structure Representation with Orientation-Aware Networks"],"prefix":"10.1177","volume":"33","author":[{"given":"Jiahan","family":"Li","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shitong","family":"Luo","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congyue","family":"Deng","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoran","family":"Cheng","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign, Urbana, Illinois, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Guan","sequence":"additional","affiliation":[{"name":"University of Illinois Urbana-Champaign, Urbana, Illinois, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonidas","family":"Guibas","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Peng","sequence":"additional","affiliation":[{"name":"Helixon Inc., Urbana, Illinois, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianzhu","family":"Ma","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China."},{"name":"Helixon Inc., Urbana, Illinois, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,12,31]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.7b00125"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0598-1"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.4750"},{"key":"e_1_3_2_5_1","first-page":"32","article-title":"Cormorant: Covariant molecular neural networks","author":"Anderson B","year":"2019","unstructured":"Anderson B, , Hy TS, , Kondor R. Cormorant: Covariant molecular neural networks. Adv Neural Inf Process Syst, 2019:32.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.abj8754"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa714"},{"key":"e_1_3_2_8_1","article-title":"Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials","author":"Batzner S","year":"2021","unstructured":"Batzner S, , Smidt TE, , Sun L, et al. Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. arXiv Preprint arXiv:2101.03164 2021.","journal-title":"arXiv Preprint"},{"key":"e_1_3_2_9_1","unstructured":"Bepler T Berger B. (2018). Learning protein sequence embeddings using information from structure. In International Conference on Learning Representations."},{"key":"e_1_3_2_10_1","article-title":"Geometric and physical quantities improve e (3) equivariant message passing","author":"Brandstetter J","year":"2021","unstructured":"Brandstetter J, , Hesselink R, , van der Pol E, et al. Geometric and physical quantities improve e (3) equivariant message passing. arXiv Preprint arXiv, 2021.","journal-title":"arXiv Preprint arXiv"},{"key":"e_1_3_2_11_1","unstructured":"Cao Y Das P Chenthamarakshan V et al. (2021). Fold2seq: A joint sequence (1d)-fold (3d) embedding-based generative model for protein design. In International Conference on Machine Learning pages 1261\u20131271. PMLR."},{"key":"e_1_3_2_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/prot.25767"},{"key":"e_1_3_2_13_1","unstructured":"Cohen T Welling M. (2016). Group equivariant convolutional networks. In International Conference on Machine Learning pages 2990\u20132999. PMLR."},{"key":"e_1_3_2_14_1","doi-asserted-by":"crossref","unstructured":"Deng C Litany O Duan Y et al. (2021). Vector neurons: A general framework for so (3)-equivariant networks. In Proceedings of the IEEE\/CVF International Conference on Computer Vision pages 12200\u201312209.","DOI":"10.1109\/ICCV48922.2021.01198"},{"key":"e_1_3_2_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty494"},{"key":"e_1_3_2_16_1","article-title":"Edge contraction pooling for graph neural networks","author":"Diehl F","year":"2019","unstructured":"Diehl F. Edge contraction pooling for graph neural networks. arXiv Preprint, 2019.","journal-title":"arXiv Preprint"},{"key":"e_1_3_2_17_1","doi-asserted-by":"crossref","unstructured":"Duhovny D Nussinov R Wolfson HJ. (2002). Efficient unbound docking of rigid molecules. In International Workshop on Algorithms in Bioinformatics pages 185\u2013200. Springer.","DOI":"10.1007\/3-540-45784-4_14"},{"key":"e_1_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/prot.26033"},{"key":"e_1_3_2_19_1","article-title":"Prottrans: Towards cracking the language of life\u2019s code through self-supervised deep learning and high performance computing","author":"Elnaggar A","year":"2020","unstructured":"Elnaggar A, , Heinzinger M, , Dallago C, et al. Prottrans: Towards cracking the language of life\u2019s code through self-supervised deep learning and high performance computing. arXiv Preprint arXiv, 2020.","journal-title":"arXiv Preprint arXiv"},{"key":"e_1_3_2_20_1","unstructured":"Fout AM. (2017). Protein interface prediction using graph convolutional networks. PhD thesis Colorado State University."},{"key":"e_1_3_2_21_1","first-page":"1970","article-title":"Se (3)-transformers: 3d roto-translation equivariant attention networks","volume":"33","author":"Fuchs F","year":"2020","unstructured":"Fuchs F, , Worrall D, , Fischer V, et al. Se (3)-transformers: 3d roto-translation equivariant attention networks. Adv Neural Inf Process Syst, 2020; 33:1970\u20131981.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_2_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0666-6"},{"key":"e_1_3_2_23_1","unstructured":"Gao H Ji S. (2019). Graph u-nets. In International Conference on Machine Learning pages 2083\u20132092. PMLR."},{"key":"e_1_3_2_24_1","unstructured":"Gasteiger J Gro\u00df J G\u00fcnnemann S. (2019a). Directional message passing for molecular graphs. In International Conference on Learning Representations."},{"key":"e_1_3_2_25_1","unstructured":"Gasteiger J Gro\u00df J G\u00fcnnemann S. (2019b). Directional message passing for molecular graphs. In International Conference on Learning Representations."},{"key":"e_1_3_2_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-23303-9"},{"key":"e_1_3_2_27_1","first-page":"1","volume-title":"International Conference on Learning Representations","author":"Hermosilla Casajus P","year":"2021","unstructured":"Hermosilla Casajus P, , Sch\u00e4fer M, , Lang M, et al. (2021). Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures. In International Conference on Learning Representations, ICLR: Vienna,Austria, pages 1\u201316. OpenReview. net."},{"key":"e_1_3_2_28_1","unstructured":"Hermosilla P Sch\u00e4fer M Lang M et al. (2020). Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures. In International Conference on Learning Representations."},{"key":"e_1_3_2_29_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx780"},{"key":"e_1_3_2_30_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab118"},{"key":"e_1_3_2_31_1","first-page":"32","article-title":"Generative models for graph-based protein design","author":"Ingraham J","year":"2019","unstructured":"Ingraham J, , Garg V, , Barzilay R, et al. Generative models for graph-based protein design. Adv Neural Inf Process Syst, 2019:32.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_2_32_1","doi-asserted-by":"publisher","DOI":"10.1121\/1.2016299"},{"key":"e_1_3_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.59"},{"key":"e_1_3_2_34_1","article-title":"Equivariant graph neural networks for 3d macromolecular structure","author":"Jing B","year":"2021","unstructured":"Jing B, , Eismann S, , Soni PN, et al. Equivariant graph neural networks for 3d macromolecular structure. ICML 2021 CompBio Workshop, 2021.","journal-title":"ICML 2021 CompBio Workshop"},{"key":"e_1_3_2_35_1","unstructured":"Jing B Eismann S Suriana P et al. (2020). Learning from protein structure with geometric vector perceptrons. In International Conference on Learning Representations."},{"key":"e_1_3_2_36_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03819-2"},{"key":"e_1_3_2_37_1","doi-asserted-by":"publisher","DOI":"10.1038\/nprot.2012.085"},{"key":"e_1_3_2_38_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty1037"},{"key":"e_1_3_2_39_1","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf TN","year":"2016","unstructured":"Kipf TN, , Welling M. Semi-supervised classification with graph convolutional networks. arXiv Preprint, 2016.","journal-title":"arXiv Preprint"},{"key":"e_1_3_2_40_1","unstructured":"K\u00f6hler J Klein L No\u00e9 F. (2020). Equivariant flows: Exact likelihood generative learning for symmetric densities. In International Conference on Machine Learning pages 5361\u20135370. PMLR."},{"key":"e_1_3_2_41_1","volume-title":"Assessment of protein model structure accuracy estimation in casp14: Old and new challenges","author":"Kwon S","year":"2021","unstructured":"Kwon S, , Won J, , Kryshtafovych A, et al. (2021). Assessment of protein model structure accuracy estimation in casp14: Old and new challenges. Proteins: Structure, Function, and Bioinformatics."},{"key":"e_1_3_2_42_1","article-title":"Hotspot-driven peptide design via multi-fragment autoregressive extension","author":"Li J","year":"2024","unstructured":"Li J, , Chen T, , Luo S, et al. Hotspot-driven peptide design via multi-fragment autoregressive extension. arXiv Preprint arXiv, 2024a.","journal-title":"arXiv Preprint arXiv"},{"key":"e_1_3_2_43_1","article-title":"Full-atom peptide design based on multi-modal flow matching","author":"Li J","year":"2024","unstructured":"Li J, , Cheng C, , Wu Z, et al. Full-atom peptide design based on multi-modal flow matching. arXiv Preprint arXiv, 2024b.","journal-title":"arXiv Preprint arXiv"},{"key":"e_1_3_2_44_1","doi-asserted-by":"crossref","unstructured":"Li S Zhou J Xu T et al. (2021). Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining pages 975\u2013985.","DOI":"10.1145\/3447548.3467311"},{"key":"e_1_3_2_45_1","doi-asserted-by":"publisher","DOI":"10.1002\/prot.24620"},{"key":"e_1_3_2_46_1","article-title":"Equiformer: Equivariant graph attention transformer for 3d atomistic graphs","author":"Liao Y-L","year":"2022","unstructured":"Liao Y-L, , Smidt T. Equiformer: Equivariant graph attention transformer for 3d atomistic graphs. arXiv Preprint arXiv, 2022.","journal-title":"arXiv Preprint arXiv"},{"key":"e_1_3_2_47_1","article-title":"Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations","author":"Liao Y-L","year":"2023","unstructured":"Liao Y-L, , Wood B, , Das A, et al. Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations. arXiv Preprint arXiv, 2023.","journal-title":"arXiv Preprint arXiv"},{"key":"e_1_3_2_48_1","unstructured":"Liu Y Wang L Liu M et al. Spherical message passing for 3d graph networks. 2021."},{"key":"e_1_3_2_49_1","doi-asserted-by":"publisher","DOI":"10.1110\/ps.08501"},{"key":"e_1_3_2_50_1","doi-asserted-by":"crossref","unstructured":"Luo S Li J Guan J et al. (2022). Equivariant point cloud analysis via learning orientations for message passing. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pages 18932\u201318941.","DOI":"10.1109\/CVPR52688.2022.01836"},{"key":"e_1_3_2_51_1","doi-asserted-by":"publisher","DOI":"10.1002\/prot.24452"},{"key":"e_1_3_2_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-2836(05)80134-2"},{"key":"e_1_3_2_53_1","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"Nair V","year":"2010","unstructured":"Nair V, , Hinton GE. Rectified linear units improve restricted Boltzmann machines. In Icml, 2010.","journal-title":"In Icml"},{"key":"e_1_3_2_54_1","volume-title":"Lehninger principles of biochemistry","author":"Nelson DL","year":"2008","unstructured":"Nelson DL, , Lehninger AL, , Cox MM. (2008). Lehninger principles of biochemistry. Macmillan."},{"key":"e_1_3_2_55_1","doi-asserted-by":"publisher","DOI":"10.1002\/prot.25489"},{"key":"e_1_3_2_56_1","doi-asserted-by":"publisher","DOI":"10.1002\/prot.25278"},{"key":"e_1_3_2_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0969-2126(97)00260-8"},{"key":"e_1_3_2_58_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz122"},{"key":"e_1_3_2_59_1","unstructured":"Passaro S Zitnick CL. (2023). Reducing SO (3) convolutions to SO (2) for efficient equivariant GNNS. In International Conference on Machine Learning pages 27420\u201327438. PMLR."},{"key":"e_1_3_2_60_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00043"},{"key":"e_1_3_2_61_1","first-page":"9689","article-title":"Evaluating protein transfer learning with tape","volume":"32","author":"Rao R","year":"2019","unstructured":"Rao R, , Bhattacharya N, , Thomas N, et al. Evaluating protein transfer learning with tape. Adv Neural Inf Process Syst, 2019; 32:9689\u20139701.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_2_62_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0076-6879(04)83004-0"},{"key":"e_1_3_2_63_1","unstructured":"Satorras VG Hoogeboom E Welling M. (2021). E (n) equivariant graph neural networks. In International Conference on Machine Learning pages 9323\u20139332. PMLR."},{"key":"e_1_3_2_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-1694(01)00466-8"},{"key":"e_1_3_2_65_1","article-title":"SchNet: A continuous-filter convolutional neural network for modeling quantum interactions","volume":"30","author":"Sch\u00fctt K","year":"2017","unstructured":"Sch\u00fctt K, , Kindermans P-J, , Sauceda Felix HE, et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. Adv Neural Inf Process Syst, 2017; 30.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_2_66_1","unstructured":"Sch\u00fctt K Unke O Gastegger M. (2021). Equivariant message passing for the prediction of tensorial properties and molecular spectra. In International Conference on Machine Learning pages 9377\u20139388. PMLR."},{"key":"e_1_3_2_67_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmb.2004.04.012"},{"key":"e_1_3_2_68_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa003"},{"key":"e_1_3_2_69_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cels.2020.08.016"},{"key":"e_1_3_2_70_1","doi-asserted-by":"crossref","unstructured":"Sverrisson F Feydy J Correia BE et al. (2021). Fast end-to-end learning on protein surfaces. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pages 15272\u201315281.","DOI":"10.1101\/2020.12.28.424589"},{"key":"e_1_3_2_71_1","article-title":"Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds","author":"Thomas N","year":"2018","unstructured":"Thomas N, , Smidt T, , Kearnes S, et al. Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds. arXiv Preprint arXiv, 2018.","journal-title":"arXiv Preprint arXiv"},{"key":"e_1_3_2_72_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-017-1702-0"},{"key":"e_1_3_2_73_1","unstructured":"Townshend RJL V\u00f6gele M Suriana PA et al. (2021). ATOM3D: Tasks on molecules in three dimensions. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track."},{"key":"e_1_3_2_74_1","first-page":"15642","article-title":"End-to-end learning on 3d protein structure for interface prediction","volume":"32","author":"Townshend R","year":"2019","unstructured":"Townshend R, , Bedi R, , Suriana P, et al. End-to-end learning on 3d protein structure for interface prediction. Adv Neural Inf Process Syst, 2019; 32:15642\u201315651.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_2_75_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btw819"},{"key":"e_1_3_2_76_1","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani A","year":"2017","unstructured":"Vaswani A, , Shazeer N, , Parmar N, et al. Attention is all you need. In Advances in Neural Information Processing Systems, 2017, pages 5998\u20136008.","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_77_1","unstructured":"Vecchio A Deac A Li\u00f2 P et al. (2021). Neural message passing for joint paratope-epitope prediction."},{"key":"e_1_3_2_78_1","unstructured":"Veli\u010dkovi\u0107 P Cucurull G Casanova A et al. (2018). Graph attention networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_79_1","volume-title":"Biochemistry","author":"Voet D","year":"2010","unstructured":"Voet D, , Voet JG. (2010). Biochemistry. John Wiley & Sons."},{"key":"e_1_3_2_80_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-24760-x"},{"key":"e_1_3_2_81_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmolb.2021.647915"},{"key":"e_1_3_2_82_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06415-8"},{"key":"e_1_3_2_83_1","volume-title":"Enzyme nomenclature 1992. Recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology on the Nomenclature and Classification of Enzymes","author":"Webb EC","year":"1992","unstructured":"Webb EC, et al. (1992). Enzyme nomenclature 1992. Recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology on the Nomenclature and Classification of Enzymes. Academic Press."},{"key":"e_1_3_2_84_1","article-title":"3D steerable CNNS: Learning rotationally equivariant features in volumetric data","volume":"31","author":"Weiler M","year":"2018","unstructured":"Weiler M, , Geiger M, , Welling M, et al. 3D steerable CNNS: Learning rotationally equivariant features in volumetric data. Adv Neural Inf Process Syst, 2018; 31.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_2_85_1","doi-asserted-by":"publisher","DOI":"10.1145\/1162349.1162350"},{"key":"e_1_3_2_86_1","unstructured":"Xu K Hu W Leskovec J et al. (2018). How powerful are graph neural networks? In International Conference on Learning Representations."},{"key":"e_1_3_2_87_1","article-title":"SE(3) diffusion model with application to protein backbone generation","author":"Yim J","year":"2023","unstructured":"Yim J, , Trippe BL, , De Bortoli V, et al. SE(3) diffusion model with application to protein backbone generation. arXiv Preprint arXiv:2302.02277 2023.","journal-title":"arXiv Preprint"},{"key":"e_1_3_2_88_1","article-title":"Hierarchical graph representation learning with differentiable pooling","volume":"31","author":"Ying Z","year":"2018","unstructured":"Ying Z, , You J, , Morris C, et al. Hierarchical graph representation learning with differentiable pooling. Adv Neural Inf Process Syst, 2018; 31.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_2_89_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkg571"},{"key":"e_1_3_2_90_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0015386"},{"key":"e_1_3_2_91_1","doi-asserted-by":"publisher","DOI":"10.1002\/prot.25868"},{"key":"e_1_3_2_92_1","doi-asserted-by":"crossref","unstructured":"Zhemchuzhnikov D Igashov I Grudinin S. (2022). 6DCNN with roto-translational convolution filters for volumetric data processing. In Proceedings of the AAAI Conference on Artificial Intelligence volume 36 pages 4707\u20134715.","DOI":"10.1609\/aaai.v36i4.20396"}],"container-title":["Journal of Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/15578666251406300","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15578666251406300","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15578666251406300","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T11:18:58Z","timestamp":1782299938000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/15578666251406300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,31]]},"references-count":91,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,1]]}},"alternative-id":["10.1177\/15578666251406300"],"URL":"https:\/\/doi.org\/10.1177\/15578666251406300","relation":{},"ISSN":["1066-5277","1557-8666"],"issn-type":[{"value":"1066-5277","type":"print"},{"value":"1557-8666","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,31]]}}}