{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T06:38:36Z","timestamp":1781678316097,"version":"3.54.5"},"reference-count":57,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T00:00:00Z","timestamp":1670716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"National Key Research and Development Project of China","award":["2021YFA1000102\uff0c2021YFA1000103"],"award-info":[{"award-number":["2021YFA1000102\uff0c2021YFA1000103"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873280"],"award-info":[{"award-number":["61873280"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972416"],"award-info":[{"award-number":["61972416"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272479"],"award-info":[{"award-number":["62272479"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202498"],"award-info":[{"award-number":["62202498"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Taishan Scholarship","award":["tsqn201812029"],"award-info":[{"award-number":["tsqn201812029"]}]},{"name":"Foundation of Science and Technology Development of Jinan","award":["201907116"],"award-info":[{"award-number":["201907116"]}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2021QF023"],"award-info":[{"award-number":["ZR2021QF023"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["21CX06018A"],"award-info":[{"award-number":["21CX06018A"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Spanish Project","award":["PID2019-106960GB-I00"],"award-info":[{"award-number":["PID2019-106960GB-I00"]}]},{"name":"Juan de la Cierva","award":["IJC2018-038539-I"],"award-info":[{"award-number":["IJC2018-038539-I"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Protein\u2013protein interactions (PPIs) are a major component of the cellular biochemical reaction network. Rich sequence information and machine learning techniques reduce the dependence of exploring PPIs on wet experiments, which are costly and time-consuming. This paper proposes a PPI prediction model, multi-scale architecture residual network for PPIs (MARPPI), based on dual-channel and multi-feature. Multi-feature leverages Res2vec to obtain the association information between residues, and utilizes pseudo amino acid composition, autocorrelation descriptors and multivariate mutual information to achieve the amino acid composition and order information, physicochemical properties and information entropy, respectively. Dual channel utilizes multi-scale architecture improved ResNet network which extracts protein sequence features to reduce protein feature loss. Compared with other advanced methods, MARPPI achieves 96.03%, 99.01% and 91.80% accuracy in the intraspecific datasets of Saccharomyces cerevisiae, Human and Helicobacter pylori, respectively. The accuracy on the two interspecific datasets of Human-Bacillus anthracis and Human-Yersinia pestis is 97.29%, and 95.30%, respectively. In addition, results on specific datasets of disease (neurodegenerative and metabolic disorders) demonstrate the ability to detect hidden interactions. To better illustrate the performance of MARPPI, evaluations on independent datasets and PPIs network suggest that MARPPI can be used to predict cross-species interactions. The above shows that MARPPI can be regarded as a concise, efficient and accurate tool for PPI datasets.<\/jats:p>","DOI":"10.1093\/bib\/bbac524","type":"journal-article","created":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T16:06:20Z","timestamp":1670774780000},"source":"Crossref","is-referenced-by-count":52,"title":["MARPPI: boosting prediction of protein\u2013protein interactions with multi-scale architecture residual network"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1489-3095","authenticated-orcid":false,"given":"Xue","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peifu","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2350-4987","authenticated-orcid":false,"given":"Wenqi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changnan","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0130-3340","authenticated-orcid":false,"given":"Tao","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muyuan","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfonso","family":"Rodriguez-Pat\u00f3n","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,12,11]]},"reference":[{"issue":"1","key":"2023011917141814300_ref1","doi-asserted-by":"crossref","first-page":"bbab545","DOI":"10.1093\/bib\/bbab545","article-title":"AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug-drug interaction prediction","volume":"23","author":"Pang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023011917141814300_ref2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.chemolab.2019.06.003","article-title":"LightGBM-PPI: predicting protein-protein interactions through LightGBM with multi-information fusion","volume":"191","author":"Chen","year":"2019","journal-title":"Chemom Intel Lab Syst"},{"issue":"2","key":"2023011917141814300_ref3","doi-asserted-by":"crossref","first-page":"bbab558","DOI":"10.1093\/bib\/bbab558","article-title":"Learning spatial structures of proteins improves protein-protein interaction prediction","volume":"23","author":"Song","year":"2022","journal-title":"Brief Bioinform"},{"issue":"6","key":"2023011917141814300_ref4","doi-asserted-by":"crossref","first-page":"bbab319","DOI":"10.1093\/bib\/bbab319","article-title":"Drug repositioning based on the heterogeneous information fusion graph convolutional network","volume":"22","author":"Cai","year":"2021","journal-title":"Brief Bioinform"},{"issue":"2","key":"2023011917141814300_ref5","doi-asserted-by":"crossref","first-page":"bbab592","DOI":"10.1093\/bib\/bbab592","article-title":"Molecular substructure tree generative model for de novo drug design","volume":"23","author":"Wang","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023011917141814300_ref6","doi-asserted-by":"crossref","first-page":"2833","DOI":"10.1109\/TSMC.2019.2917215","article-title":"A community structure enhancement-based community detection algorithm for complex networks","volume":"51","author":"Su","year":"2021","journal-title":"IEEE Trans Syst Man Cybernet-Syst"},{"key":"2023011917141814300_ref7","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1109\/TETCI.2020.3014923","article-title":"EMODMI: A multi-objective optimization based method to identify disease modules","volume":"5","author":"Tian","year":"2021","journal-title":"IEEE Trans Emerg Topics Comput Intell"},{"key":"2023011917141814300_ref8","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1021\/acssynbio.0c00472","article-title":"Quantitative yeast-yeast two hybrid for the discovery and binding affinity estimation of protein-protein interactions","volume":"10","author":"Bacon","year":"2021","journal-title":"ACS Synth Biol"},{"key":"2023011917141814300_ref9","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1021\/acs.chemrev.0c00884","article-title":"Analytical and biochemical perspectives of protein O-GlcNAcylation","volume":"121","author":"Ma","year":"2021","journal-title":"Chem Rev"},{"issue":"8","key":"2023011917141814300_ref10","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.3390\/biom11081119","article-title":"MCN-CPI: multiscale convolutional network for compound-protein interaction prediction","volume":"11","author":"Wang","year":"2021","journal-title":"Biomolecules"},{"key":"2023011917141814300_ref11","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1109\/TNB.2019.2930647","article-title":"A Heuristic algorithm for identifying molecular signatures in cancer","volume":"19","author":"Su","year":"2020","journal-title":"IEEE Trans Nanobioscience"},{"issue":"7","key":"2023011917141814300_ref12","doi-asserted-by":"crossref","first-page":"e1009165","DOI":"10.1371\/journal.pcbi.1009165","article-title":"SCMFMDA: predicting microRNA-disease associations based on similarity constrained matrix factorization","volume":"17","author":"Li","year":"2021","journal-title":"PLoS Comput Biol"},{"key":"2023011917141814300_ref13","doi-asserted-by":"crossref","DOI":"10.1109\/TCBB.2021.3113122","article-title":"Extra trees method for predicting lncRNA-disease association based on multi-layer graph embedding aggregation","author":"Wu","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2023011917141814300_ref14","doi-asserted-by":"crossref","DOI":"10.1109\/TCBB.2021.3126641","article-title":"scCDG: a method based on DAE and GCN for scRNA-seq data analysis","author":"Wang","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2023011917141814300_ref15","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1186\/s12864-022-08687-2","article-title":"SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction","volume":"23","author":"Li","year":"2022","journal-title":"BMC Genomics"},{"issue":"1","key":"2023011917141814300_ref16","first-page":"1","article-title":"Adaptive coding for DNA storage with high storage density and low coverage","volume":"8","author":"Ben","year":"2022","journal-title":"NPJ Syst Biol Appl"},{"key":"2023011917141814300_ref17","first-page":"4337","volume-title":"Proc Natl Acad Sci USA","author":"Shen","year":"2007"},{"key":"2023011917141814300_ref18","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1093\/nar\/gkn159","article-title":"Using support vector machine combined with auto covariance to predict proteinprotein interactions from protein sequences","volume":"36","author":"Guo","year":"2008","journal-title":"Nucleic Acids Res"},{"key":"2023011917141814300_ref19","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.2174\/092986610791760306","article-title":"Prediction of protein-protein interactions from protein sequence using local descriptors","volume":"17","author":"Yang","year":"2010","journal-title":"Protein Pept Lett"},{"key":"2023011917141814300_ref20","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1039\/C7MB00188F","article-title":"Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network","volume":"13","author":"Wang","year":"2017","journal-title":"Mol Biosyst"},{"key":"2023011917141814300_ref21","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1093\/bioinformatics\/bty573","article-title":"Predicting protein-protein interactions through sequence-based deep learning","volume":"34","author":"Hashemifar","year":"2018","journal-title":"Bioinformatics"},{"issue":"8","key":"2023011917141814300_ref22","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.3390\/molecules23081923","article-title":"Deep neural network based predictions of protein interactions using primary sequences","volume":"23","author":"Li","year":"2018","journal-title":"Molecules"},{"key":"2023011917141814300_ref23","doi-asserted-by":"crossref","first-page":"e7126","DOI":"10.7717\/peerj.7126","article-title":"An integration of deep learning with feature embedding for protein-protein interaction prediction","volume":"7","author":"Yao","year":"2019","journal-title":"Peerj"},{"issue":"8","key":"2023011917141814300_ref24","first-page":"1","article-title":"Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis","volume":"14","author":"You","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2023011917141814300_ref25","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1021\/acs.jcim.7b00028","article-title":"DeepPPI: boosting prediction of protein-protein interactions with deep neural networks","volume":"57","author":"Du","year":"2017","journal-title":"J Chem Inf Model"},{"issue":"11","key":"2023011917141814300_ref26","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.3390\/ijms18112373","article-title":"Protein-protein interactions prediction using a novel local conjoint triad descriptor of amino acid sequences","volume":"18","author":"Wang","year":"2017","journal-title":"Int J Mol Sci"},{"key":"2023011917141814300_ref27","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.neucom.2018.03.062","article-title":"Prediction of protein-protein interactions using an effective sequence based combined method","volume":"303","author":"Goktepe","year":"2018","journal-title":"Neurocomputing"},{"key":"2023011917141814300_ref28","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.neucom.2018.02.097","article-title":"Protein-protein interactions prediction based on ensemble deep neural networks","volume":"324","author":"Zhang","year":"2019","journal-title":"Neurocomputing"},{"key":"2023011917141814300_ref29","doi-asserted-by":"crossref","first-page":"390","DOI":"10.3389\/fbioe.2020.00390","article-title":"Protein interaction network reconstruction through ensemble deep learning with attention mechanism","volume":"8","author":"Li","year":"2020","journal-title":"Front Bioeng Biotechnol"},{"key":"2023011917141814300_ref30","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114876","article-title":"Prediction of protein-protein interactions based on elastic net and deep forest","volume":"176","author":"Yu","year":"2021","journal-title":"Expert Syst Appl"},{"key":"2023011917141814300_ref31","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TCBB.2018.2882423","article-title":"An efficient ensemble learning approach for predicting protein-protein interactions by integrating protein primary sequence and evolutionary information","volume":"16","author":"You","year":"2019","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2023011917141814300_ref32","first-page":"1","volume-title":"2020 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), India","author":"Mahapatra","year":"2020"},{"key":"2023011917141814300_ref33","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1021\/acs.jcim.1c00173","article-title":"Predicting protein-protein interactions using symmetric logistic matrix factorization","volume":"61","author":"Pei","year":"2021","journal-title":"J Chem Inf Model"},{"key":"2023011917141814300_ref34","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103964","article-title":"AE-LGBM: Sequence-based novel approach to detect interacting protein pairs via ensemble of autoencoder and LightGBM","volume":"125","author":"Sharma","year":"2020","journal-title":"Comput Biol Med"},{"key":"2023011917141814300_ref35","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.1093\/bioinformatics\/btv077","article-title":"Evolutionary profiles improve protein-protein interaction prediction from sequence","volume":"31","author":"Hamp","year":"2015","journal-title":"Bioinformatics"},{"key":"2023011917141814300_ref36","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1093\/bioinformatics\/bty933","article-title":"OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction","volume":"35","author":"Smaili","year":"2019","journal-title":"Bioinformatics"},{"key":"2023011917141814300_ref37","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1002\/prot.1035","article-title":"Prediction of protein cellular attributes using pseudo-amino acid composition","volume":"43","author":"Chou","year":"2001","journal-title":"Proteins"},{"key":"2023011917141814300_ref38","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.gpb.2021.01.001","article-title":"GTB-PPI: predict protein-protein interactions based on L1-regularized logistic regression and gradient tree boosting","volume":"18","author":"Yu","year":"2020","journal-title":"Genomics Proteomics Bioinformatics"},{"key":"2023011917141814300_ref39","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"He","year":"2016"},{"key":"2023011917141814300_ref40","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2Net: a new multi-scale backbone architecture","volume":"43","author":"Gao","year":"2021","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2023011917141814300_ref41","volume-title":"IEEE\/ACM Trans Computat Biol Bioinform","author":"Dey","year":"2022"},{"key":"2023011917141814300_ref42","first-page":"62","article-title":"Kappa coefficient: a popular measure of rater agreement","volume":"27","author":"Tang","year":"2015","journal-title":"Shanghai Arch Psychiatry"},{"key":"2023011917141814300_ref43","article-title":"A high efficient biological language model for predicting protein-protein interactions","volume":"8","author":"Wang","year":"2019","journal-title":"Cell"},{"key":"2023011917141814300_ref44","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103899","article-title":"Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier","volume":"123","author":"Chen","year":"2020","journal-title":"Comput Biol Med"},{"issue":"1","key":"2023011917141814300_ref45","doi-asserted-by":"crossref","first-page":"89","DOI":"10.3390\/app8010089","article-title":"An ensemble classifier with random projection for predicting protein-protein interactions using sequence and evolutionary information","volume":"8","author":"Song","year":"2018","journal-title":"Appl Sci"},{"key":"2023011917141814300_ref46","doi-asserted-by":"crossref","first-page":"1176934319844522","DOI":"10.1177\/1176934319844522","article-title":"Sequence-based prediction of protein-protein interactions using gray wolf optimizer-based relevance vector machine","volume":"15","author":"An","year":"2019","journal-title":"Evol Bioinform"},{"key":"2023011917141814300_ref47","first-page":"40","volume-title":"IEEE-ACM Trans Comput Biol Bioinform","author":"Qian","year":"2022"},{"key":"2023011917141814300_ref48","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.compbiolchem.2018.12.001","article-title":"A new sequence based encoding for prediction of host-pathogen protein interactions","volume":"78","author":"Kosesoy","year":"2019","journal-title":"Comput Biol Chem"},{"key":"2023011917141814300_ref49","doi-asserted-by":"crossref","first-page":"12976","DOI":"10.1074\/jbc.M510617200","article-title":"Contrasting effects of EWI proteins, integrins, and protein palmitoylation on cell surface CD9 organization","volume":"281","author":"Yang","year":"2006","journal-title":"J Biol Chem"},{"key":"2023011917141814300_ref50","first-page":"587","article-title":"Molecular genetics and targeted therapy of WNT-related human diseases","volume":"40","author":"Katoh","year":"2017","journal-title":"Int J Mol Med"},{"key":"2023011917141814300_ref51","doi-asserted-by":"crossref","first-page":"D561","DOI":"10.1093\/nar\/gkl958","article-title":"IntAct - open source resource for molecular interaction data","volume":"35","author":"Kerrien","year":"2007","journal-title":"Nucleic Acids Res"},{"key":"2023011917141814300_ref52","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.ymeth.2022.02.007","article-title":"DeepFusion: a deep learning based multi-scale feature fusion method for predicting drug-target interactions","volume":"204","author":"Song","year":"2022","journal-title":"Methods"},{"key":"2023011917141814300_ref53","article-title":"Multi-TransDTI: transformer for drug-target interaction prediction based on simple universal dictionaries with multi-view strategy","volume":"12","author":"Wang","year":"2022","journal-title":"Biomolecules"},{"key":"2023011917141814300_ref54","doi-asserted-by":"crossref","first-page":"105214","DOI":"10.1016\/j.compbiomed.2022.105214","article-title":"DeepMGT-DTI: transformer network incorporating multilayer graph information for drug-target interaction prediction","volume":"142","author":"Zhang","year":"2022","journal-title":"Comput Biol Med"},{"key":"2023011917141814300_ref55","doi-asserted-by":"crossref","first-page":"4771","DOI":"10.1093\/bioinformatics\/btab533","article-title":"Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction","volume":"37","author":"Yang","year":"2021","journal-title":"Bioinformatics"},{"key":"2023011917141814300_ref56","doi-asserted-by":"crossref","first-page":"64606","DOI":"10.1109\/ACCESS.2021.3074243","article-title":"Review of classification methods on unbalanced data sets","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"issue":"4","key":"2023011917141814300_ref57","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac285","article-title":"De novo molecular design with deep molecular generative models for PPI inhibitors","volume":"23","author":"Wang","year":"2022","journal-title":"Brief Bioinform"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac524\/48782557\/bbac524.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac524\/48782557\/bbac524.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T17:45:20Z","timestamp":1674150320000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac524\/6887309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,11]]},"references-count":57,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac524","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,1]]},"published":{"date-parts":[[2022,12,11]]},"article-number":"bbac524"}}