{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T16:02:15Z","timestamp":1775750535641,"version":"3.50.1"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"24","license":[{"start":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T00:00:00Z","timestamp":1626480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFC1200205"],"award-info":[{"award-number":["2017YFC1200205"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFD0500404"],"award-info":[{"award-number":["2017YFD0500404"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>To complement experimental efforts, machine learning-based computational methods are playing an increasingly important role to predict human\u2013virus protein\u2013protein interactions (PPIs). Furthermore, transfer learning can effectively apply prior knowledge obtained from a large source dataset\/task to a small target dataset\/task, improving prediction performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To predict interactions between human and viral proteins, we combine evolutionary sequence profile features with a Siamese convolutional neural network (CNN) architecture and a multi-layer perceptron. Our architecture outperforms various feature encodings-based machine learning and state-of-the-art prediction methods. As our main contribution, we introduce two transfer learning methods (i.e. \u2018frozen\u2019 type and \u2018fine-tuning\u2019 type) that reliably predict interactions in a target human\u2013virus domain based on training in a source human\u2013virus domain, by retraining CNN layers. Finally, we utilize the \u2018frozen\u2019 type transfer learning approach to predict human\u2013SARS-CoV-2 PPIs, indicating that our predictions are topologically and functionally similar to experimentally known interactions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source codes and datasets are available at https:\/\/github.com\/XiaodiYangCAU\/TransPPI\/.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab533","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T07:17:37Z","timestamp":1626419857000},"page":"4771-4778","source":"Crossref","is-referenced-by-count":68,"title":["Transfer learning via multi-scale convolutional neural layers for human\u2013virus protein\u2013protein interaction prediction"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3229-5865","authenticated-orcid":false,"given":"Xiaodi","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University , Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5631-3549","authenticated-orcid":false,"given":"Shiping","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University , Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7576-2198","authenticated-orcid":false,"given":"Xianyi","family":"Lian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University , Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8916-6522","authenticated-orcid":false,"given":"Stefan","family":"Wuchty","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Miami , Miami, FL 33146, USA"},{"name":"Department of Biology, University of Miami , Miami, FL 33146, USA"},{"name":"Sylvester Comprehensive Cancer Center, University of Miami , Miami, FL 33136, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9296-571X","authenticated-orcid":false,"given":"Ziding","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University , Beijing 100193, China"}]}],"member":"286","published-online":{"date-parts":[[2021,7,17]]},"reference":[{"key":"2023051607145917700_btab533-B1","doi-asserted-by":"crossref","first-page":"4159","DOI":"10.1093\/bioinformatics\/bty504","article-title":"Prediction of human-Bacillus anthracis protein\u2013protein interactions using multi-layer neural network","volume":"34","author":"Ahmed","year":"2018","journal-title":"Bioinformatics"},{"key":"2023051607145917700_btab533-B2","doi-asserted-by":"crossref","first-page":"D408","DOI":"10.1093\/nar\/gkw985","article-title":"HIPPIE v2.0: enhancing meaningfulness and reliability of protein\u2013protein interaction networks","volume":"45","author":"Alanis-Lobato","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023051607145917700_btab533-B3","doi-asserted-by":"crossref","first-page":"1391265","DOI":"10.1155\/2018\/1391265","article-title":"Predicting interactions between virus and host proteins using repeat patterns and composition of amino acids","volume":"2018","author":"Alguwaizani","year":"2018","journal-title":"J. Healthc. Eng"},{"key":"2023051607145917700_btab533-B4","first-page":"D271","article-title":"The RCSB protein data bank: integrative view of protein, gene and 3D structural information","volume":"45","author":"Altunkaya","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023051607145917700_btab533-B5","doi-asserted-by":"crossref","first-page":"baw103","DOI":"10.1093\/database\/baw103","article-title":"HPIDB 2.0: a curated database for host-pathogen interactions","volume":"2016","author":"Ammari","year":"2016","journal-title":"Database"},{"key":"2023051607145917700_btab533-B6","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/1471-2105-4-2","article-title":"An automated method for finding molecular complexes in large protein interaction networks","volume":"4","author":"Bader","year":"2003","journal-title":"BMC Bioinformatics"},{"key":"2023051607145917700_btab533-B7","doi-asserted-by":"crossref","first-page":"D588","DOI":"10.1093\/nar\/gku830","article-title":"VirusMentha: a new resource for virus-host protein interactions","volume":"43","author":"Calderone","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023051607145917700_btab533-B8","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1109\/TPAMI.2017.2656884","article-title":"Unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications","volume":"40","author":"Chang","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"2023051607145917700_btab533-B9","doi-asserted-by":"crossref","first-page":"i305","DOI":"10.1093\/bioinformatics\/btz328","article-title":"Multifaceted protein\u2013protein interaction prediction based on Siamese residual RCNN","volume":"35","author":"Chen","year":"2019","journal-title":"Bioinformatics"},{"key":"2023051607145917700_btab533-B10","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1186\/1471-2105-13-S7-S5","article-title":"Prediction of protein\u2013protein interactions between viruses and human by an SVM model","volume":"13","author":"Cui","year":"2012","journal-title":"BMC Bioinformatics"},{"key":"2023051607145917700_btab533-B11","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1093\/bioinformatics\/btn382","article-title":"Optimizing amino acid groupings for GPCR classification","volume":"24","author":"Davies","year":"2008","journal-title":"Bioinformatics"},{"key":"2023051607145917700_btab533-B12","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1021\/acs.jcim.7b00028","article-title":"DeepPPI: boosting prediction of protein\u2013protein interactions with deep neural networks","volume":"57","author":"Du","year":"2017","journal-title":"J. Chem. Inf. Model"},{"key":"2023051607145917700_btab533-B13","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1093\/bioinformatics\/btt137","article-title":"PHISTO: pathogen\u2013host interaction search tool","volume":"29","author":"Durmu\u015f Tekir","year":"2013","journal-title":"Bioinformatics"},{"key":"2023051607145917700_btab533-B14","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.meegid.2011.02.022","article-title":"Supervised learning and prediction of physical interactions between human and HIV proteins","volume":"11","author":"Dyer","year":"2011","journal-title":"Infect. Genet. Evol"},{"key":"2023051607145917700_btab533-B15","doi-asserted-by":"crossref","first-page":"e32","DOI":"10.1371\/journal.ppat.0040032","article-title":"The landscape of human proteins interacting with viruses and other pathogens","volume":"4","author":"Dyer","year":"2008","journal-title":"PLoS Pathog"},{"key":"2023051607145917700_btab533-B16","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1093\/bioinformatics\/btv737","article-title":"DeNovo: virus-host sequence-based protein\u2013protein interaction prediction","volume":"32","author":"Eid","year":"2016","journal-title":"Bioinformatics"},{"key":"2023051607145917700_btab533-B17","doi-asserted-by":"crossref","first-page":"3147","DOI":"10.1039\/C4MB00410H","article-title":"Predicting protein\u2013protein interactions between human and hepatitis C virus via an ensemble learning method","volume":"10","author":"Emamjomeh","year":"2014","journal-title":"Mol. Biosyst"},{"key":"2023051607145917700_btab533-B18","doi-asserted-by":"crossref","first-page":"D559","DOI":"10.1093\/nar\/gky973","article-title":"CORUM: the comprehensive resource of mammalian protein complexes \u2013 2019","volume":"47","author":"Giurgiu","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023051607145917700_btab533-B19","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1038\/s41586-020-2286-9","article-title":"A SARS-CoV-2 protein interaction map reveals targets for drug repurposing","volume":"583","author":"Gordon","year":"2020","journal-title":"Nature"},{"key":"2023051607145917700_btab533-B20","doi-asserted-by":"crossref","first-page":"D583","DOI":"10.1093\/nar\/gku1121","article-title":"VirHostNet 2.0: surfing on the web of virus\/host molecular interactions data","volume":"43","author":"Guirimand","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023051607145917700_btab533-B21","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1093\/nar\/gkn159","article-title":"Using support vector machine combined with auto covariance to predict protein\u2013protein interactions from protein sequences","volume":"36","author":"Guo","year":"2008","journal-title":"Nucleic Acids Res"},{"key":"2023051607145917700_btab533-B22","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.1093\/bioinformatics\/btv077","article-title":"Evolutionary profiles improve protein\u2013protein interaction prediction from sequence","volume":"31","author":"Hamp","year":"2015","journal-title":"Bioinformatics"},{"key":"2023051607145917700_btab533-B23","doi-asserted-by":"crossref","first-page":"i802","DOI":"10.1093\/bioinformatics\/bty573","article-title":"Predicting protein\u2013protein interactions through sequence-based deep learning","volume":"34","author":"Hashemifar","year":"2018","journal-title":"Bioinformatics"},{"key":"2023051607145917700_btab533-B24","first-page":"1188","article-title":"Distributed representations of sentences and documents","volume":"14","author":"Le","year":"2014","journal-title":"Proc. Int. Conf. Mach. Learn"},{"key":"2023051607145917700_btab533-B25","first-page":"609","article-title":"Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations","volume":"54","author":"Lee","year":"2009","journal-title":"Proc. 26th Int. Conf. Mach. Learn"},{"key":"2023051607145917700_btab533-B26","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.medj.2020.07.002","article-title":"Virus-host interactome and proteomic survey reveal potential virulence factors influencing SARS-CoV-2 pathogenesis","volume":"2","author":"Li","year":"2021","journal-title":"Med"},{"key":"2023051607145917700_btab533-B27","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab029","article-title":"Current status and future perspectives of computational studies on human-virus protein\u2013protein interactions","author":"Lian","year":"2021","journal-title":"Brief. Bioinform"},{"key":"2023051607145917700_btab533-B28","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1007\/s40484-020-0222-5","article-title":"Prediction and analysis of human-herpes simplex virus type 1 protein\u2013protein interactions by integrating multiple methods","volume":"8","author":"Lian","year":"2020","journal-title":"Quant. Biol"},{"key":"2023051607145917700_btab533-B29","doi-asserted-by":"crossref","first-page":"2722","DOI":"10.1093\/bioinformatics\/btab147","article-title":"DeepViral: prediction of novel virus\u2013host interactions from protein sequences and infectious disease phenotypes","volume":"37","author":"Liu-Wei","year":"2021","journal-title":"Bioinformatics"},{"key":"2023051607145917700_btab533-B30","doi-asserted-by":"crossref","first-page":"e01156-17","DOI":"10.1128\/JVI.01156-17","article-title":"Role of herpes simplex virus 1 \u03b334.5 in the regulation of IRF3 signaling","volume":"91","author":"Manivanh","year":"2017","journal-title":"J. Virol"},{"key":"2023051607145917700_btab533-B31","doi-asserted-by":"crossref","first-page":"i92","DOI":"10.1093\/bioinformatics\/btx234","article-title":"Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding","volume":"33","author":"Min","year":"2017","journal-title":"Bioinformatics"},{"key":"2023051607145917700_btab533-B32","doi-asserted-by":"crossref","first-page":"3069","DOI":"10.1128\/JVI.01875-08","article-title":"Ebola virus protein VP35 impairs the function of interferon regulatory factor-activating kinases IKK\u03b5 and TBK-1","volume":"83","author":"Prins","year":"2009","journal-title":"J. Virol"},{"key":"2023051607145917700_btab533-B33","first-page":"1","author":"Reddi","year":"2018"},{"key":"2023051607145917700_btab533-B34","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.1016\/j.cell.2018.11.028","article-title":"Comparative flavivirus-host protein interaction mapping reveals mechanisms of dengue and Zika virus pathogenesis","volume":"175","author":"Shah","year":"2018","journal-title":"Cell"},{"key":"2023051607145917700_btab533-B35","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/TNNLS.2014.2330900","article-title":"Transfer learning for visual categorization: a survey","volume":"26","author":"Shao","year":"2015","journal-title":"IEEE Trans. Neural Networks Learn. Syst"},{"key":"2023051607145917700_btab533-B36","doi-asserted-by":"crossref","first-page":"4337","DOI":"10.1073\/pnas.0607879104","article-title":"Predicting protein\u2013protein interactions based only on sequences information","volume":"104","author":"Shen","year":"2007","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023051607145917700_btab533-B37","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1186\/s12859-017-1700-2","article-title":"Sequence-based prediction of protein protein interaction using a deep-learning algorithm","volume":"18","author":"Sun","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"2023051607145917700_btab533-B38","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1093\/bioinformatics\/btu739","article-title":"UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches","volume":"31","author":"Suzek","year":"2015","journal-title":"Bioinformatics"},{"key":"2023051607145917700_btab533-B39","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.cels.2019.04.003","article-title":"MultiPLIER: a transfer learning framework for transcriptomics reveals systemic features of rare disease","volume":"8","author":"Taroni","year":"2019","journal-title":"Cell Syst"},{"key":"2023051607145917700_btab533-B40","doi-asserted-by":"crossref","first-page":"D158","DOI":"10.1093\/nar\/gkw1099","article-title":"UniProt: the universal protein knowledgebase","volume":"45","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2023051607145917700_btab533-B41","doi-asserted-by":"crossref","first-page":"13640","DOI":"10.1073\/pnas.0502883102","article-title":"Viral targeting of the interferon-\u03b2-inducing Traf family member-associated NF-\u03baB activator (TANK)-binding kinase-1","volume":"102","author":"Unterstab","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023051607145917700_btab533-B42","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1074\/jbc.M805905200","article-title":"Control of TANK-binding kinase 1-mediated signaling by the \u03b3134.5 protein of herpes simplex virus 1","volume":"284","author":"Verpooten","year":"2009","journal-title":"J. Biol. Chem"},{"key":"2023051607145917700_btab533-B43","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1093\/bioinformatics\/btn583","article-title":"Prediction of DNA-binding residues in proteins from amino acid sequences using a random forest model with a hybrid feature","volume":"25","author":"Wu","year":"2009","journal-title":"Bioinformatics"},{"key":"2023051607145917700_btab533-B44","doi-asserted-by":"crossref","first-page":"e11796","DOI":"10.1371\/journal.pone.0011796","article-title":"Viral organization of human proteins","volume":"5","author":"Wuchty","year":"2010","journal-title":"PLoS One"},{"key":"2023051607145917700_btab533-B45","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.2174\/092986610791760306","article-title":"Prediction of protein\u2013protein interactions from protein sequence using local descriptors","volume":"17","author":"Yang","year":"2010","journal-title":"Protein Pept. Lett"},{"key":"2023051607145917700_btab533-B46","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1093\/bib\/bbaa425","article-title":"HVIDB: a comprehensive database for human-virus protein\u2013protein interactions","volume":"22","author":"Yang","year":"2021","journal-title":"Brief. Bioinform"},{"key":"2023051607145917700_btab533-B47","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.csbj.2019.12.005","article-title":"Prediction of human-virus protein\u2013protein interactions through a sequence embedding-based machine learning method","volume":"18","author":"Yang","year":"2020","journal-title":"Comput. Struct. Biotechnol. J"},{"key":"2023051607145917700_btab533-B48","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1186\/1471-2105-14-S8-S10","article-title":"Prediction of protein\u2013protein 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":"2023051607145917700_btab533-B49","doi-asserted-by":"crossref","first-page":"e32","DOI":"10.1093\/nar\/gkv1025","article-title":"A deep learning framework for modeling structural features of RNA-binding protein targets","volume":"44","author":"Zhang","year":"2016","journal-title":"Nucleic Acids Res"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab533\/39809663\/btab533.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/24\/4771\/50334929\/btab533.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/24\/4771\/50334929\/btab533.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T03:46:07Z","timestamp":1684208767000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/24\/4771\/6323357"}},"subtitle":[],"editor":[{"given":"Teresa","family":"Przytycka","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,7,17]]},"references-count":49,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2021,12,11]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab533","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.02.16.431420","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,12,15]]},"published":{"date-parts":[[2021,7,17]]}}}