{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:10:02Z","timestamp":1781583002144,"version":"3.54.5"},"reference-count":67,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T00:00:00Z","timestamp":1697760000000},"content-version":"vor","delay-in-days":28,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"National Key Research Program","award":["2021YFA0910700"],"award-info":[{"award-number":["2021YFA0910700"]}]},{"name":"Shenzhen Science and Technology University stable support program","award":["GXWD20201230155427003-20200821222112001"],"award-info":[{"award-number":["GXWD20201230155427003-20200821222112001"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82003553"],"award-info":[{"award-number":["82003553"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Key Area Research Program","award":["2020B0101380001"],"award-info":[{"award-number":["2020B0101380001"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20200109113201726"],"award-info":[{"award-number":["JCYJ20200109113201726"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Protein function annotation is one of the most important research topics for revealing the essence of life at molecular level in the post-genome era. Current research shows that integrating multisource data can effectively improve the performance of protein function prediction models. However, the heavy reliance on complex feature engineering and model integration methods limits the development of existing methods. Besides, models based on deep learning only use labeled data in a certain dataset to extract sequence features, thus ignoring a large amount of existing unlabeled sequence data. Here, we propose an end-to-end protein function annotation model named HNetGO, which innovatively uses heterogeneous network to integrate protein sequence similarity and protein\u2013protein interaction network information and combines the pretraining model to extract the semantic features of the protein sequence. In addition, we design an attention-based graph neural network model, which can effectively extract node-level features from heterogeneous networks and predict protein function by measuring the similarity between protein nodes and gene ontology term nodes. Comparative experiments on the human dataset show that HNetGO achieves state-of-the-art performance on cellular component and molecular function branches.<\/jats:p>","DOI":"10.1093\/bib\/bbab556","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T20:13:22Z","timestamp":1638821602000},"source":"Crossref","is-referenced-by-count":26,"title":["HNetGO: protein function prediction via heterogeneous network transformer"],"prefix":"10.1093","volume":"24","author":[{"given":"Xiaoshuai","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen) , Shenzhen, Guangdong 518055 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huannan","family":"Guo","sequence":"additional","affiliation":[{"name":"General Hospital of Heilongjiang Province Land Reclamation Bureau , Harbin 150086 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center NHC Key Laboratory of Cell Transplantation, The First Affiliated Hospital of Harbin Medical University , Harbin 150086 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen) , Shenzhen, Guangdong 518055 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaitao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen) , Shenzhen, Guangdong 518055 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0047-4199","authenticated-orcid":false,"given":"Shizheng","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Harbin 150001 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Harbin 150001 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yadong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen) , Shenzhen, Guangdong 518055 , China"},{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Harbin 150001 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4508-5365","authenticated-orcid":false,"given":"Yang","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology , Harbin 150001 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen) , Shenzhen, Guangdong 518055 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,10,19]]},"reference":[{"issue":"D1","key":"2023102011014883900_ref1","doi-asserted-by":"crossref","first-page":"D330","DOI":"10.1093\/nar\/gky1055","article-title":"The gene ontology resource: 20 years and still GOing strong","volume":"47","author":"Consortium GO","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"2023102011014883900_ref2","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/TCBB.2010.38","article-title":"True path rule hierarchical ensembles for genome-wide gene function prediction","volume":"8","author":"Valentini","year":"2010","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"6","key":"2023102011014883900_ref3","doi-asserted-by":"crossref","first-page":"611","DOI":"10.2174\/1574893615999200504103643","article-title":"Rosetta and the journey to predict proteins' structures, 20 years on","volume":"15","author":"Abbass","year":"2020","journal-title":"Curr Bioinform"},{"issue":"11","key":"2023102011014883900_ref4","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1093\/bioinformatics\/bty002","article-title":"DincRNA: a comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function","volume":"34","author":"Cheng","year":"2018","journal-title":"Bioinformatics"},{"issue":"22","key":"2023102011014883900_ref5","doi-asserted-by":"crossref","first-page":"3685","DOI":"10.1093\/bioinformatics\/btx531","article-title":"An introduction to deep learning on biological sequence data: examples and solutions","volume":"33","author":"Jurtz","year":"2017","journal-title":"Bioinformatics"},{"issue":"7792","key":"2023102011014883900_ref6","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1038\/s41586-019-1923-7","article-title":"Improved protein structure prediction using potentials from deep learning","volume":"577","author":"Senior","year":"2020","journal-title":"Nature"},{"key":"2023102011014883900_ref7","doi-asserted-by":"crossref","first-page":"7036592","DOI":"10.1155\/2021\/7036592","article-title":"Integration of multiple-omics data to analyze the population-specific differences for coronary artery disease","volume":"2021","author":"Hu","year":"2021","journal-title":"Comput Math Methods Med"},{"key":"2023102011014883900_ref8","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/978-3-319-65981-7_12","volume-title":"Classification in BioApps","author":"Razzak","year":"2018"},{"issue":"suppl_2","key":"2023102011014883900_ref9","doi-asserted-by":"crossref","first-page":"W214","DOI":"10.1093\/nar\/gkq537","article-title":"The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function","volume":"38","author":"Warde-Farley","year":"2010","journal-title":"Nucleic Acids Res"},{"issue":"Suppl 3","key":"2023102011014883900_ref10","doi-asserted-by":"crossref","first-page":"S8","DOI":"10.1186\/1471-2105-14-S3-S8","article-title":"MS-kNN: protein function prediction by integrating multiple data sources","volume":"14","author":"Lan","year":"2013","journal-title":"BMC Bioinformatics"},{"issue":"W1","key":"2023102011014883900_ref11","doi-asserted-by":"crossref","first-page":"W379","DOI":"10.1093\/nar\/gkz388","article-title":"NetGO: improving large-scale protein function prediction with massive network information","volume":"47","author":"You","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"16","key":"2023102011014883900_ref12","doi-asserted-by":"crossref","first-page":"4466","DOI":"10.1093\/bioinformatics\/btaa428","article-title":"DeepLGP: a novel deep learning method for prioritizing lncRNA target genes","volume":"36","author":"Zhao","year":"2020","journal-title":"Bioinformatics"},{"issue":"4","key":"2023102011014883900_ref13","doi-asserted-by":"crossref","first-page":"210","DOI":"10.2174\/156652321904191022113307","article-title":"Computational and biological methods for gene therapy","volume":"19","author":"Cheng","year":"2019","journal-title":"Curr Gene Ther"},{"key":"2023102011014883900_ref14","doi-asserted-by":"crossref","first-page":"107238","DOI":"10.1016\/j.compbiolchem.2020.107238","article-title":"Computational prediction of protein ubiquitination sites mapping on Arabidopsis thaliana","volume":"85","author":"Mosharaf","year":"2020","journal-title":"Comput Biol Chem"},{"issue":"4","key":"2023102011014883900_ref15","doi-asserted-by":"crossref","first-page":"368","DOI":"10.2174\/1574893614666191105155713","article-title":"ConvsPPIS: identifying protein-protein interaction sites by an ensemble convolutional neural network with feature graph","volume":"15","author":"Zhu","year":"2020","journal-title":"Curr Bioinform"},{"issue":"4","key":"2023102011014883900_ref16","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1093\/bioinformatics\/btx624","article-title":"DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier","volume":"34","author":"Kulmanov","year":"2018","journal-title":"Bioinformatics"},{"issue":"2","key":"2023102011014883900_ref17","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1093\/bioinformatics\/btz595","article-title":"DeepGOPlus: improved protein function prediction from sequence","volume":"36","author":"Kulmanov","year":"2020","journal-title":"Bioinformatics"},{"issue":"22","key":"2023102011014883900_ref18","doi-asserted-by":"crossref","first-page":"3873","DOI":"10.1093\/bioinformatics\/bty440","article-title":"deepNF: deep network fusion for protein function prediction","volume":"34","author":"Gligorijevi\u0107","year":"2018","journal-title":"Bioinformatics"},{"issue":"2","key":"2023102011014883900_ref19","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1093\/bib\/bbaa036","article-title":"Integrating multi-network topology for gene function prediction using deep neural networks","volume":"22","author":"Peng","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023102011014883900_ref20","first-page":"29","volume-title":"2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea","author":"Li"},{"key":"2023102011014883900_ref21","first-page":"1836","volume-title":"2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA","author":"Zhou"},{"issue":"14","key":"2023102011014883900_ref22","doi-asserted-by":"crossref","first-page":"1900119","DOI":"10.1002\/pmic.201900119","article-title":"Protein function prediction: from traditional classifier to deep learning","volume":"19","author":"Lv","year":"2019","journal-title":"Proteomics"},{"issue":"18","key":"2023102011014883900_ref23","doi-asserted-by":"crossref","first-page":"2825\u201333","DOI":"10.1093\/bioinformatics\/btab198","article-title":"TALE: transformer-based protein function annotation with joint sequence-label embedding","volume":"37","author":"Cao","year":"2021","journal-title":"Bioinformatics"},{"issue":"822","key":"2023102011014883900_ref24","doi-asserted-by":"crossref","first-page":"748722","DOI":"10.3389\/fbioe.2021.748722","article-title":"Editorial: feature representation and learning methods with applications in protein secondary structure","volume":"9","author":"Yan","year":"2021","journal-title":"Front Bioeng Biotechnol"},{"issue":"1","key":"2023102011014883900_ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-3220-8","article-title":"Modeling aspects of the language of life through transfer-learning protein sequences","volume":"20","author":"Heinzinger","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"24","key":"2023102011014883900_ref26","doi-asserted-by":"crossref","first-page":"5600","DOI":"10.1093\/bioinformatics\/btaa1074","article-title":"Identification of sub-Golgi protein localization by use of deep representation learning features","volume":"36","author":"Lv","year":"2020","journal-title":"Bioinformatics"},{"key":"2023102011014883900_ref27","doi-asserted-by":"crossref","first-page":"bbab1008","DOI":"10.1093\/bib\/bbab008","article-title":"Anticancer peptides prediction with deep representation learning features","volume":"22","author":"Lv","year":"2021","journal-title":"Brief Bioinform"},{"issue":"30","key":"2023102011014883900_ref28","doi-asserted-by":"crossref","first-page":"47864","DOI":"10.18632\/oncotarget.10012","article-title":"IntNetLncSim: an integrative network analysis method to infer human lncRNA functional similarity","volume":"7","author":"Cheng","year":"2016","journal-title":"Oncotarget"},{"key":"2023102011014883900_ref29","volume-title":"BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding","author":"Devlin","year":"2018"},{"key":"2023102011014883900_ref30","volume-title":"XLNet: Generalized Autoregressive Pretraining for Language Understanding","author":"Yang","year":"2019"},{"issue":"11","key":"2023102011014883900_ref31","doi-asserted-by":"crossref","first-page":"e0141287","DOI":"10.1371\/journal.pone.0141287","article-title":"Continuous distributed representation of biological sequences for deep proteomics and genomics","volume":"10","author":"Asgari","year":"2015","journal-title":"PLoS One"},{"issue":"12","key":"2023102011014883900_ref32","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1038\/s41592-019-0598-1","article-title":"Unified rational protein engineering with sequence-based deep representation learning","volume":"16","author":"Alley","year":"2019","journal-title":"Nat Methods"},{"issue":"15","key":"2023102011014883900_ref33","doi-asserted-by":"crossref","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2023102011014883900_ref34","article-title":"ProtTrans: towards cracking the language of Life's code through self-supervised deep learning and high performance computing","author":"Elnaggar","year":"2020","journal-title":"arXiv preprint arXiv:200706225"},{"key":"2023102011014883900_ref35","article-title":"Bag of tricks for efficient text classification","author":"Joulin","year":"2016","journal-title":"arXiv preprint arXiv:160701759"},{"key":"2023102011014883900_ref36","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov","year":"2013","journal-title":"arXiv preprint arXiv:13013781"},{"issue":"8","key":"2023102011014883900_ref37","doi-asserted-by":"crossref","first-page":"949","DOI":"10.2174\/1574893615666200204112347","article-title":"Deep novo a plus: improving the deep learning model for de novo peptide sequencing with additional ion types and validation set","volume":"15","author":"Di","year":"2020","journal-title":"Curr Bioinform"},{"issue":"4","key":"2023102011014883900_ref38","doi-asserted-by":"crossref","first-page":"300","DOI":"10.2174\/1574893614666190902154332","article-title":"Predicting protein phosphorylation sites based on deep learning","volume":"15","author":"Long","year":"2020","journal-title":"Curr Bioinform"},{"issue":"7","key":"2023102011014883900_ref39","doi-asserted-by":"crossref","first-page":"662","DOI":"10.2174\/1574893614666190723121610","article-title":"Natural scene nutrition information acquisition and analysis based on deep learning","volume":"15","author":"Zhang","year":"2020","journal-title":"Curr Bioinform"},{"issue":"8","key":"2023102011014883900_ref40","doi-asserted-by":"crossref","first-page":"898","DOI":"10.2174\/1574893615999200711165743","article-title":"Review of the applications of deep learning in bioinformatics","volume":"15","author":"Zhang","year":"2020","journal-title":"Curr Bioinform"},{"issue":"3","key":"2023102011014883900_ref41","doi-asserted-by":"crossref","first-page":"466","DOI":"10.2174\/1574893615999200707143535","article-title":"Deep learning model for pathogen classification using feature fusion and data augmentation","volume":"16","author":"Ahmad","year":"2021","journal-title":"Curr Bioinform"},{"issue":"D1","key":"2023102011014883900_ref42","doi-asserted-by":"crossref","first-page":"D506","DOI":"10.1093\/nar\/gky1049","article-title":"UniProt: a worldwide hub of protein knowledge","volume":"47","author":"Consortium U","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2023102011014883900_ref43","doi-asserted-by":"crossref","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"STRING v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"Szklarczyk","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"17","key":"2023102011014883900_ref44","doi-asserted-by":"crossref","first-page":"2556","DOI":"10.1093\/bioinformatics\/btab133","article-title":"BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides","volume":"37","author":"Charoenkwan","year":"2021","journal-title":"Bioinformatics"},{"issue":"11","key":"2023102011014883900_ref45","doi-asserted-by":"crossref","first-page":"3350","DOI":"10.1093\/bioinformatics\/btaa160","article-title":"HLPpred-fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation","volume":"36","author":"Hasan","year":"2020","journal-title":"Bioinformatics"},{"issue":"4096","key":"2023102011014883900_ref46","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1126\/science.181.4096.223","article-title":"Principles that govern the folding of protein chains","volume":"181","author":"Anfinsen","year":"1973","journal-title":"Science"},{"key":"2023102011014883900_ref47","article-title":"Deep contextualized word representations","author":"Peters","year":"2018","journal-title":"arXiv preprint arXiv:180205365"},{"issue":"1","key":"2023102011014883900_ref48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-80786-0","article-title":"Embeddings from deep learning transfer GO annotations beyond homology","volume":"11","author":"Littmann","year":"2021","journal-title":"Sci Rep"},{"issue":"2","key":"2023102011014883900_ref49","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1093\/bioinformatics\/btaa701","article-title":"Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function","volume":"37","author":"Villegas-Morcillo","year":"2021","journal-title":"Bioinformatics"},{"issue":"6","key":"2023102011014883900_ref50","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1093\/bioinformatics\/btu739","article-title":"Consortium U: UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches","volume":"31","author":"Suzek","year":"2015","journal-title":"Bioinformatics"},{"key":"2023102011014883900_ref51","first-page":"9689","article-title":"Evaluating protein transfer learning with tape","volume":"32","author":"Rao","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"issue":"7","key":"2023102011014883900_ref52","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1093\/nar\/30.7.1575","article-title":"An efficient algorithm for large-scale detection of protein families","volume":"30","author":"Enright","year":"2002","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"2023102011014883900_ref53","doi-asserted-by":"crossref","first-page":"e33","DOI":"10.1093\/nar\/gkx1313","article-title":"HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks","volume":"46","author":"Azad","year":"2018","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2023102011014883900_ref54","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/nmeth.3176","article-title":"Fast and sensitive protein alignment using DIAMOND","volume":"12","author":"Buchfink","year":"2015","journal-title":"Nat Methods"},{"issue":"4","key":"2023102011014883900_ref55","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1038\/s41592-021-01101-x","article-title":"Sensitive protein alignments at tree-of-life scale using DIAMOND","volume":"18","author":"Buchfink","year":"2021","journal-title":"Nat Methods"},{"key":"2023102011014883900_ref56","article-title":"ProSPr: democratized implementation of alphafold protein distance prediction network","volume":"830273","author":"Billings","year":"2019","journal-title":"BioRxiv"},{"key":"2023102011014883900_ref57","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"arXiv preprint arXiv:170603762"},{"key":"2023102011014883900_ref58","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017","journal-title":"arXiv preprint arXiv:171010903"},{"key":"2023102011014883900_ref59","first-page":"2704","volume-title":"Proceedings of The Web Conference, Taipei, Taiwan","author":"Hu","year":"2020"},{"key":"2023102011014883900_ref60","doi-asserted-by":"crossref","first-page":"391","DOI":"10.3389\/fbioe.2020.00391","article-title":"SDN2GO: an integrated deep learning model for protein function prediction","volume":"8","author":"Cai","year":"2020","journal-title":"Front Bioeng Biotechnol"},{"issue":"4","key":"2023102011014883900_ref61","doi-asserted-by":"crossref","first-page":"bbaa275","DOI":"10.1093\/bib\/bbaa275","article-title":"Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework","volume":"22","author":"Wei","year":"2021","journal-title":"Brief Bioinform"},{"issue":"6","key":"2023102011014883900_ref62","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab167","article-title":"NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning","volume":"22","author":"Hasan","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023102011014883900_ref63","doi-asserted-by":"crossref","first-page":"bbab172","DOI":"10.1093\/bib\/bbab172","article-title":"StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides","volume":"22","author":"Charoenkwan","year":"2021","journal-title":"Brief Bioinform"},{"issue":"4","key":"2023102011014883900_ref64","doi-asserted-by":"crossref","first-page":"216","DOI":"10.2174\/1566523219666190924113737","article-title":"Identifying Alzheimer's disease-related miRNA based on semi-clustering","volume":"19","author":"Zhao","year":"2019","journal-title":"Curr Gene Ther"},{"issue":"4","key":"2023102011014883900_ref65","doi-asserted-by":"crossref","first-page":"224","DOI":"10.2174\/1566523219666190925115535","article-title":"A Mendelian randomization study on infant length and type 2 diabetes mellitus risk","volume":"19","author":"Zhuang","year":"2019","journal-title":"Curr Gene Ther"},{"issue":"1","key":"2023102011014883900_ref66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1835-8","article-title":"The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens","volume":"20","author":"Zhou","year":"2019","journal-title":"Genome Biol"},{"issue":"17","key":"2023102011014883900_ref67","doi-asserted-by":"crossref","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"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/6\/bbab556\/52263157\/bbab556.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/6\/bbab556\/52263157\/bbab556.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T11:04:12Z","timestamp":1697799852000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab556\/7323467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,22]]},"references-count":67,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,9,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab556","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,11,1]]},"published":{"date-parts":[[2023,9,22]]},"article-number":"bbab556"}}