{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T02:35:57Z","timestamp":1785897357040,"version":"3.56.0"},"reference-count":70,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":305,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFA0712400"],"award-info":[{"award-number":["2020YFA0712400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62,272,268"],"award-info":[{"award-number":["62,272,268"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Identification of protein\u2013protein and protein\u2013nucleic acid binding sites provides insights into biological processes related to protein functions and technical guidance for disease diagnosis and drug design. However, accurate predictions by computational approaches remain highly challenging due to the limited knowledge of residue binding patterns. The binding pattern of a residue should be characterized by the spatial distribution of its neighboring residues combined with their physicochemical information interaction, which yet cannot be achieved by previous methods. Here, we design GraphRBF, a hierarchical geometric deep learning model to learn residue binding patterns from big data. To achieve it, GraphRBF describes physicochemical information interactions by designing an enhanced graph neural network and characterizes residue spatial distributions by introducing a prioritized radial basis function neural network. After training and testing, GraphRBF shows great improvements over existing state-of-the-art methods and strong interpretability of its learned representations. Applying GraphRBF to the SARS-CoV-2 omicron spike protein, it successfully identifies known epitopes of the protein. Moreover, it predicts multiple potential binding regions for new nanobodies or even new drugs with strong evidence. A user-friendly online server for GraphRBF is freely available at http:\/\/liulab.top\/GraphRBF\/server.<\/jats:p>","DOI":"10.1093\/gigascience\/giae080","type":"journal-article","created":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T10:22:16Z","timestamp":1730456536000},"source":"Crossref","is-referenced-by-count":5,"title":["Protein\u2013protein and protein\u2013nucleic acid binding site prediction via interpretable hierarchical geometric deep learning"],"prefix":"10.1093","volume":"13","author":[{"given":"Shizhuo","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Shandong University (Weihai) , Weihai 264209 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyun","family":"Han","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Shandong University (Weihai) , Weihai 264209 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7296-906X","authenticated-orcid":false,"given":"Juntao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Shandong University (Weihai) , Weihai 264209 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"issue":"6761, Suppl","key":"2024111706291969100_bib1","doi-asserted-by":"publisher","first-page":"C47","DOI":"10.1038\/35011540","article-title":"From molecular to modular cell biology","volume":"402","author":"Hartwell","year":"1999","journal-title":"Nature"},{"issue":"21","key":"2024111706291969100_bib2","doi-asserted-by":"publisher","first-page":"7364","DOI":"10.1093\/nar\/gkq617","article-title":"Genomic repertoires of DNA-binding transcription factors across the tree of life","volume":"38","author":"Charoensawan","year":"2010","journal-title":"Nucleic Acids Res"},{"issue":"7218","key":"2024111706291969100_bib3","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1038\/nature07348","article-title":"Stepwise chromatin remodelling by a cascade of transcription initiation of non-coding RNAs","volume":"456","author":"Hirota","year":"2008","journal-title":"Nature"},{"issue":"4","key":"2024111706291969100_bib4","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1186\/gm441","article-title":"Protein-protein interaction networks: probing disease mechanisms using model systems","volume":"5","author":"Kuzmanov","year":"2013","journal-title":"Genome Med"},{"issue":"7172","key":"2024111706291969100_bib5","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1038\/nature06526","article-title":"Reaching for high-hanging fruit in drug discovery at protein-protein interfaces","volume":"450","author":"Wells","year":"2007","journal-title":"Nature"},{"issue":"10","key":"2024111706291969100_bib6","doi-asserted-by":"publisher","first-page":"gkx059","DOI":"10.1093\/nar\/gkx059","article-title":"DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues","volume":"45","author":"Yan","year":"2017","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2024111706291969100_bib7","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1093\/bib\/bbv027","article-title":"Progress and challenges in predicting protein interfaces","volume":"17","author":"Esmaielbeiki","year":"2016","journal-title":"Brief Bioinform"},{"issue":"14","key":"2024111706291969100_bib8","doi-asserted-by":"publisher","first-page":"i343","DOI":"10.1093\/bioinformatics\/btz324","article-title":"SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences","volume":"35","author":"Zhang","year":"2019","journal-title":"Bioinformatics"},{"issue":"6","key":"2024111706291969100_bib9","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1109\/tcbb.2016.2616469","article-title":"Predicting protein-DNA binding residues by weightedly combining sequence-based features and boosting multiple SVMs","volume":"14","author":"Hu","year":"2017","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"2024111706291969100_bib10","doi-asserted-by":"publisher","first-page":"e25","DOI":"10.1093\/nar\/gkac1253","article-title":"HybridRNAbind: prediction of RNA interacting residues across structure-annotated and disorder-annotated proteins","volume":"51","author":"Zhang","year":"2023","journal-title":"Nucleic Acids Res"},{"issue":"2","key":"2024111706291969100_bib11","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.bbapap.2016.11.008","article-title":"Understand protein functions by comparing the similarity of local structural environments","volume":"1865","author":"Chen","year":"2017","journal-title":"Biochim Biophys Acta Proteins Proteom"},{"issue":"9","key":"2024111706291969100_bib12","doi-asserted-by":"publisher","first-page":"e51","DOI":"10.1093\/nar\/gkab044","article-title":"GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues","volume":"49","author":"Xia","year":"2021","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"2024111706291969100_bib13","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1038\/s41592-022-01490-7","article-title":"ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction","volume":"19","author":"Tubiana","year":"2022","journal-title":"Nat Methods"},{"issue":"2","key":"2024111706291969100_bib14","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1038\/s41592-019-0666-6","article-title":"Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning","volume":"17","author":"Gainza","year":"2020","journal-title":"Nat Methods"},{"issue":"4","key":"2024111706291969100_bib15","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1093\/bioinformatics\/btz699","article-title":"Protein-protein interaction site prediction through combining local and global features with deep neural networks","volume":"36","author":"Zeng","year":"2020","journal-title":"Bioinformatics"},{"issue":"9","key":"2024111706291969100_bib16","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.1038\/s42256-023-00715-4","article-title":"Evaluating protein binding interfaces with transformer networks","volume":"5","author":"Stebliankin","year":"2023","journal-title":"Nat Mach Intell"},{"issue":"6","key":"2024111706291969100_bib17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbad345","article-title":"Spatom: a graph neural network for structure-based protein-protein interaction site prediction","volume":"24","author":"Wu","year":"2023","journal-title":"Brief Bioinform"},{"issue":"4","key":"2024111706291969100_bib18","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1002\/prot.10221","article-title":"Data mining the protein data bank: residue interactions","volume":"49","author":"Oldfield","year":"2002","journal-title":"Proteins"},{"issue":"3","key":"2024111706291969100_bib19","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1093\/bioinformatics\/btaa714","article-title":"GraphQA: protein model quality assessment using graph convolutional networks","volume":"37","author":"Baldassarre","year":"2021","journal-title":"Bioinformatics"},{"issue":"1","key":"2024111706291969100_bib20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbab474","article-title":"A point cloud-based deep learning strategy for protein-ligand binding affinity prediction","volume":"23","author":"Wang","year":"2022","journal-title":"Brief Bioinform"},{"issue":"32","key":"2024111706291969100_bib21","doi-asserted-by":"publisher","first-page":"7503","DOI":"10.1039\/c9sc02097g","article-title":"A generalized deep learning approach for local structure identification in molecular simulations","volume":"10","author":"DeFever","year":"2019","journal-title":"Chem Sci"},{"key":"2024111706291969100_bib22","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1109\/CVPR.2017.16","article-title":"PointNet: deep learning on point sets for 3D classification and segmentation","volume-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Qi","year":"2017"},{"issue":"10","key":"2024111706291969100_bib23","doi-asserted-by":"publisher","first-page":"e60","DOI":"10.1093\/nar\/gkad288","article-title":"GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning","volume":"51","author":"Li","year":"2023","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"2024111706291969100_bib24","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1002\/prot.21248","article-title":"Prediction-based fingerprints of protein-protein interactions","volume":"66","author":"Porollo","year":"2007","journal-title":"Proteins"},{"issue":"1","key":"2024111706291969100_bib25","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1093\/bioinformatics\/btab643","article-title":"Structure-aware protein-protein interaction site prediction using deep graph convolutional network","volume":"38","author":"Yuan","year":"2021","journal-title":"Bioinformatics"},{"issue":"1","key":"2024111706291969100_bib26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbab521","article-title":"DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning","volume":"23","author":"Zhang","year":"2022","journal-title":"Briefings Bioinf"},{"key":"2024111706291969100_bib27","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1162\/neco.1991.3.2.246","article-title":"Universal approximation using radial-basis-function networks","volume":"3","author":"Park","year":"1991","journal-title":"Neural Comput"},{"issue":"2","key":"2024111706291969100_bib28","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1109\/72.80341","article-title":"Orthogonal least squares learning algorithm for radial basis function networks","volume":"2","author":"Chen","year":"1991","journal-title":"IEEE Trans Neural Netw"},{"issue":"9","key":"2024111706291969100_bib29","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"Mathews","year":"2009","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2024111706291969100_bib30","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1214\/aos\/1176342997","article-title":"Optimal convergence properties of variable knot, kernel, and orthogonal series methods for density estimation","volume":"3","author":"Wahba","year":"1975","journal-title":"Ann Stat"},{"key":"2024111706291969100_bib31","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1111\/j.2517-6161.1991.tb01857.x","article-title":"A reliable data-based bandwidth selection method for kernel density estimation","volume":"53","author":"Sheather","year":"1991","journal-title":"J R Stat Soc Ser B Methodol"},{"key":"2024111706291969100_bib32","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2302.02451","article-title":"KDEformer: accelerating transformers via kernel density estimation","author":"Zandieh","year":"2023"},{"key":"2024111706291969100_bib33","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"issue":"1","key":"2024111706291969100_bib34","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/0022-2836(82)90515-0","article-title":"A simple method for displaying the hydropathic character of a protein","volume":"157","author":"Kyte","year":"1982","journal-title":"J Mol Biol"},{"key":"2024111706291969100_bib35","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1186\/1471-2105-9-553","article-title":"Prediction of protein-protein binding site by using core interface residue and support vector machine","volume":"9","author":"Li","year":"2008","journal-title":"BMC Bioinf"},{"issue":"2","key":"2024111706291969100_bib36","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.cell.2020.02.058","article-title":"Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein","volume":"181","author":"Walls","year":"2020","journal-title":"Cell"},{"issue":"6483","key":"2024111706291969100_bib37","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1126\/science.abb2507","article-title":"Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation","volume":"367","author":"Wrapp","year":"2020","journal-title":"Science"},{"issue":"1","key":"2024111706291969100_bib38","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1038\/s41467-022-28882-9","article-title":"Cryo-EM structure of a SARS-CoV-2 omicron spike protein ectodomain","volume":"13","author":"Ye","year":"2022","journal-title":"Nat Commun"},{"issue":"10","key":"2024111706291969100_bib39","doi-asserted-by":"publisher","first-page":"1635","DOI":"10.1038\/s41564-022-01235-4","article-title":"SARS-CoV-2 Delta and Omicron variants evade population antibody response by mutations in a single spike epitope","volume":"7","author":"He","year":"2022","journal-title":"Nat Microbiol"},{"issue":"7807","key":"2024111706291969100_bib40","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1038\/s41586-020-2180-5","article-title":"Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor","volume":"581","author":"Lan","year":"2020","journal-title":"Nature"},{"key":"2024111706291969100_bib41","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.bbrc.2021.12.082","article-title":"Structural modeling of Omicron spike protein and its complex with human ACE-2 receptor: molecular basis for high transmissibility of the virus","volume":"592","author":"Koley","year":"2022","journal-title":"Biochem Biophys Res Commun"},{"issue":"11","key":"2024111706291969100_bib42","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1016\/j.molmed.2022.08.007","article-title":"Nanobodies as a powerful platform for biomedicine","volume":"28","author":"Shen","year":"2022","journal-title":"Trends Mol Med"},{"key":"2024111706291969100_bib43","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.3389\/fonc.2020.01182","article-title":"Nanobodies: next generation of cancer diagnostics and therapeutics","volume":"10","author":"Yang","year":"2020","journal-title":"Front Oncol"},{"issue":"11","key":"2024111706291969100_bib44","doi-asserted-by":"publisher","first-page":"e0144823","DOI":"10.1128\/jvi.01448-23","article-title":"Discovery of Nanosota-2, -3, and -4 as super potent and broad-spectrum therapeutic nanobody candidates against COVID-19","volume":"97","author":"Ye","year":"2023","journal-title":"J Virol"},{"issue":"1","key":"2024111706291969100_bib45","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1093\/nar\/28.1.235","article-title":"The Protein Data Bank","volume":"28","author":"Berman","year":"2000","journal-title":"Nucleic Acids Res"},{"issue":"5","key":"2024111706291969100_bib46","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1016\/j.chom.2021.03.005","article-title":"Potent SARS-CoV-2 neutralizing antibodies directed against spike N-terminal domain target a single supersite","volume":"29","author":"Cerutti","year":"2021","journal-title":"Cell Host Microbe"},{"issue":"1","key":"2024111706291969100_bib47","doi-asserted-by":"publisher","first-page":"4198","DOI":"10.1038\/s41467-023-39890-8","article-title":"Structural delineation and computational design of SARS-CoV-2-neutralizing antibodies against Omicron subvariants","volume":"14","author":"Moriyama","year":"2023","journal-title":"Nat Commun"},{"key":"2024111706291969100_bib67","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1002\/pro.3235","article-title":"UCSF ChimeraX: meeting modern challenges in visualization and analysis","volume":"27","author":"Goddard","year":"2018","journal-title":"Protein Sci"},{"key":"2024111706291969100_bib48","doi-asserted-by":"publisher","first-page":"894531","DOI":"10.3389\/fviro.2022.894531","article-title":"Omicron spike protein has a positive electrostatic surface that promotes ACE2 recognition and antibody escape","volume":"2","author":"Gan","year":"2022","journal-title":"Front Virol"},{"issue":"6520","key":"2024111706291969100_bib49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1126\/science.abd4250","article-title":"Viral epitope profiling of COVID-19 patients reveals cross-reactivity and correlates of severity","volume":"370","author":"Shrock","year":"2020","journal-title":"Science"},{"issue":"4","key":"2024111706291969100_bib50","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1007\/s00134-020-05985-9","article-title":"Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: molecular mechanisms and potential therapeutic target","volume":"46","author":"Zhang","year":"2020","journal-title":"Intensive Care Med"},{"issue":"19","key":"2024111706291969100_bib51","doi-asserted-by":"publisher","first-page":"3031","DOI":"10.1016\/j.jmb.2015.07.016","article-title":"Updates to the integrated protein-protein interaction benchmarks: docking benchmark version 5 and affinity Benchmark version 2","volume":"427","author":"Vreven","year":"2015","journal-title":"J Mol Biol"},{"issue":"1","key":"2024111706291969100_bib52","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1002\/pro.3295","article-title":"Dockground: a comprehensive data resource for modeling of protein complexes","volume":"27","author":"Kundrotas","year":"2018","journal-title":"Protein Sci"},{"issue":"D1","key":"2024111706291969100_bib53","doi-asserted-by":"publisher","first-page":"D404","DOI":"10.1093\/nar\/gkad630","article-title":"BioLiP2: an updated structure database for biologically relevant ligand-protein interactions","volume":"52","author":"Zhang","year":"2024","journal-title":"Nucleic Acids Res"},{"issue":"12","key":"2024111706291969100_bib54","doi-asserted-by":"publisher","first-page":"2577","DOI":"10.1002\/bip.360221211","article-title":"Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features","volume":"22","author":"Kabsch","year":"1983","journal-title":"Biopolymers"},{"issue":"17","key":"2024111706291969100_bib55","doi-asserted-by":"publisher","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res"},{"issue":"10","key":"2024111706291969100_bib56","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1093\/bioinformatics\/btm098","article-title":"UniRef: comprehensive and non-redundant UniProt reference clusters","volume":"23","author":"Suzek","year":"2007","journal-title":"Bioinformatics"},{"issue":"2","key":"2024111706291969100_bib57","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1038\/nmeth.1818","article-title":"HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment","volume":"9","author":"Remmert","year":"2012","journal-title":"Nat Methods"},{"issue":"D1","key":"2024111706291969100_bib58","doi-asserted-by":"publisher","first-page":"D170","DOI":"10.1093\/nar\/gkw1081","article-title":"Uniclust databases of clustered and deeply annotated protein sequences and alignments","volume":"45","author":"Mirdita","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2024111706291969100_bib59","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1502.03167","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015","journal-title":"arXiv preprint\u00a0arXiv:150203167"},{"key":"2024111706291969100_bib60","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","volume-title":"Proceedings of the 27th International Conference on Machine Learning","author":"Nair","year":"2010"},{"key":"2024111706291969100_bib61","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"2024111706291969100_bib62","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.03762","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"2024111706291969100_bib63","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.02413","article-title":"PointNet++: deep hierarchical feature learning on point sets in a metric space","author":"Qi","year":"2017"},{"key":"2024111706291969100_bib64","article-title":"Generative models for graph-based protein design","volume-title":"Advances in Neural Information Processing Systems","author":"Ingraham","year":"2019"},{"key":"2024111706291969100_bib65","doi-asserted-by":"publisher","first-page":"045005","DOI":"10.1088\/2632-2153\/abf856","article-title":"Spherical convolutions on molecular graphs for protein model quality assessment","volume":"2","author":"Igashov","year":"2021","journal-title":"Mach Learn Sci Technol"},{"key":"2024111706291969100_bib66","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1708.02002","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"},{"key":"2024111706291969100_bib68","unstructured":"Zhang S, Han J, Liu J. The processed data for training and testing GraphRBF and other compared models (Version v1). Zenodo. 2024. 10.5281\/zenodo.10826801. Accessed\u00a020\u00a0September 2024."},{"key":"2024111706291969100_bib69","article-title":"The source code files for reproducing and evaluating GraphRBF","author":"Zhang","year":"2024","journal-title":"GitHub"},{"key":"2024111706291969100_bib70_515_114524","doi-asserted-by":"publisher","DOI":"10.48546\/WORKFLOWHUB.WORKFLOW.1107.1","article-title":"Protein-protein and protein-nucleic acid binding site prediction via interpretable hierarchical geometric deep learning","volume-title":"WorkflowHub","author":"Zhang","year":"2024"}],"container-title":["GigaScience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/gigascience\/article-pdf\/doi\/10.1093\/gigascience\/giae080\/60704502\/giae080.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/gigascience\/article-pdf\/doi\/10.1093\/gigascience\/giae080\/60704502\/giae080.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T06:30:38Z","timestamp":1731825038000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/gigascience\/article\/doi\/10.1093\/gigascience\/giae080\/7824699"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":70,"URL":"https:\/\/doi.org\/10.1093\/gigascience\/giae080","relation":{},"ISSN":["2047-217X"],"issn-type":[{"value":"2047-217X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024]]},"published":{"date-parts":[[2024]]},"article-number":"giae080"}}