{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T22:34:11Z","timestamp":1774305251194,"version":"3.50.1"},"reference-count":71,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"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\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071278"],"award-info":[{"award-number":["62071278"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072329"],"award-info":[{"award-number":["62072329"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["18H03250"],"award-info":[{"award-number":["18H03250"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001863","name":"New Energy and Industrial Technology Development Organization","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001863","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Peptides have recently emerged as promising therapeutic agents against various diseases. For both research and safety regulation purposes, it is of high importance to develop computational methods to accurately predict the potential toxicity of peptides within the vast number of candidate peptides. Results: In this study, we proposed ATSE, a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural networks and attention mechanism. More specifically, it consists of four modules: (i) a sequence processing module for converting peptide sequences to molecular graphs and evolutionary profiles, (ii) a feature extraction module designed to learn discriminative features from graph structural information and evolutionary information, (iii) an attention module employed to optimize the features and (iv) an output module determining a peptide as toxic or non-toxic, using optimized features from the attention module. Conclusion: Comparative studies demonstrate that the proposed ATSE significantly outperforms all other competing methods. We found that structural information is complementary to the evolutionary information, effectively improving the predictive performance. Importantly, the data-driven features learned by ATSE can be interpreted and visualized, providing additional information for further analysis. Moreover, we present a user-friendly online computational platform that implements the proposed ATSE, which is now available at http:\/\/server.malab.cn\/ATSE. We expect that it can be a powerful and useful tool for researchers of interest.<\/jats:p>","DOI":"10.1093\/bib\/bbab041","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T20:12:34Z","timestamp":1611951154000},"source":"Crossref","is-referenced-by-count":103,"title":["ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism"],"prefix":"10.1093","volume":"22","author":[{"given":"Lesong","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577"}]},{"given":"Xiucai","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577"}]},{"given":"Yuyang","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577"}]},{"given":"Tetsuya","family":"Sakurai","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1444-190X","authenticated-orcid":false,"given":"Leyi","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}]}],"member":"286","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"key":"2021090815124904200_ref1","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1111\/cbdd.12055","article-title":"The future of peptide-based drugs","volume":"81","author":"Craik","year":"2013","journal-title":"Chem Biol Drug Des"},{"key":"2021090815124904200_ref2","article-title":"Peptides as drug candidates: limitations and recent development perspectives","volume":"1","author":"Haggag","year":"2018","journal-title":"Biomed J"},{"key":"2021090815124904200_ref3","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1016\/j.soard.2014.01.032","article-title":"Effects on GLP-1, PYY, and leptin by direct stimulation of terminal ileum and cecum in humans: implications for ileal transposition","volume":"10","author":"Buchwald","year":"2014","journal-title":"Surg Obes Relat Dis"},{"key":"2021090815124904200_ref4","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.drudis.2014.10.003","article-title":"Peptide therapeutics: current status and future directions","volume":"20","author":"Fosgerau","year":"2015","journal-title":"Drug Discov Today"},{"key":"2021090815124904200_ref5","doi-asserted-by":"crossref","first-page":"63","DOI":"10.3389\/fneur.2014.00063","article-title":"Neuroactive peptides as putative mediators of antiepileptic ketogenic diets","volume":"5","author":"Giordano","year":"2014","journal-title":"Front Neurol"},{"key":"2021090815124904200_ref6","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.tube.2014.03.011","article-title":"Antimicrobial peptides and proteins in mycobacterial therapy: current status and future prospects","volume":"94","author":"Padhi","year":"2014","journal-title":"Tuberculosis"},{"key":"2021090815124904200_ref7","article-title":"A comprehensive review on current advances in peptide drug development and design","volume":"20","author":"","year":"2019","journal-title":"Int J Molecular Ences"},{"key":"2021090815124904200_ref8","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/978-1-4419-7210-1_36","volume-title":"Advances in Systems Biology","author":"Benson","year":"2012"},{"key":"2021090815124904200_ref9","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1111\/j.1476-5381.2009.00190.x","article-title":"Therapeutic antibodies: successes, limitations and hopes for the future","volume":"157","author":"Chames","year":"2009","journal-title":"Br J Pharmacol"},{"key":"2021090815124904200_ref10","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s12929-017-0328-x","article-title":"Evaluation of the use of therapeutic peptides for cancer treatment","volume":"24","author":"Marqus","year":"2017","journal-title":"J Biomed Sci"},{"key":"2021090815124904200_ref11","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.drudis.2009.10.009","article-title":"Synthetic therapeutic peptides: science and market","volume":"15","author":"Vlieghe","year":"2010","journal-title":"Drug Discov Today"},{"key":"2021090815124904200_ref12","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/978-1-4939-2285-7_7","volume-title":"Computational Peptidology","author":"Gupta","year":"2015"},{"key":"2021090815124904200_ref13","doi-asserted-by":"crossref","first-page":"3185","DOI":"10.2174\/138161210793292555","article-title":"Chemical modifications designed to improve peptide stability: incorporation of non-natural amino acids, pseudo-peptide bonds, and cyclization","volume":"16","author":"Gentilucci","year":"2010","journal-title":"Curr Pharm Des"},{"key":"2021090815124904200_ref14","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s00726-011-1095-8","article-title":"Extraordinary metabolic stability of peptides containing \u03b1-aminoxy acids","volume":"43","author":"Chen","year":"2012","journal-title":"Amino Acids"},{"key":"2021090815124904200_ref15","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/1745-6150-8-27","article-title":"Identification of B-cell epitopes in an antigen for inducing specific class of antibodies","volume":"8","author":"Gupta","year":"2013","journal-title":"Biol Direct"},{"key":"2021090815124904200_ref16","first-page":"10","article-title":"Structure based prediction of neoantigen immunogenicity","volume":"2019","author":"Riley","year":"2047","journal-title":"Front Immunol"},{"key":"2021090815124904200_ref17","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1038\/nature14001","article-title":"Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing","volume":"515","author":"Yadav","year":"2014","journal-title":"Nature"},{"key":"2021090815124904200_ref18","doi-asserted-by":"crossref","first-page":"W363","DOI":"10.1093\/nar\/gkp299","article-title":"ClanTox: a classifier of short animal toxins","volume":"37","author":"Naamati","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2021090815124904200_ref19","doi-asserted-by":"crossref","first-page":"e73957","DOI":"10.1371\/journal.pone.0073957","article-title":"In silico approach for predicting toxicity of peptides and proteins","volume":"8","author":"Gupta","year":"2013","journal-title":"PLoS One"},{"key":"2021090815124904200_ref20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12864-017-4128-1","article-title":"SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides","volume":"18","author":"Wei","year":"2017","journal-title":"BMC Genomics"},{"key":"2021090815124904200_ref21","doi-asserted-by":"crossref","first-page":"190","DOI":"10.2174\/1574893614666181212102749","article-title":"A review on the recent developments of sequence-based protein feature extraction methods","volume":"14","author":"Zhang","year":"2019","journal-title":"Curr Bioinforma"},{"key":"2021090815124904200_ref22","doi-asserted-by":"crossref","first-page":"2466","DOI":"10.3934\/mbe.2019123","article-title":"Identification of hormone binding proteins based on machine learning methods","volume":"16","author":"Tan","year":"2019","journal-title":"Math Biosci Eng"},{"key":"2021090815124904200_ref23","doi-asserted-by":"crossref","first-page":"957","DOI":"10.7150\/ijbs.24174","article-title":"HBPred: a tool to identify growth hormone-binding proteins","volume":"14","author":"Tang","year":"2018","journal-title":"Int J Biol Sci"},{"key":"2021090815124904200_ref24","doi-asserted-by":"crossref","first-page":"2982","DOI":"10.1093\/bioinformatics\/btz040","article-title":"Protein fold recognition based on multi-view modeling","volume":"35","author":"Yan","year":"2019","journal-title":"Bioinformatics"},{"key":"2021090815124904200_ref25","volume-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics","author":"Yan","year":"2020"},{"key":"2021090815124904200_ref26","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1073\/pnas.1621344114","article-title":"Sequence-, structure-, and dynamics-based comparisons of structurally homologous CheY-like proteins","volume":"114","author":"He","year":"2017","journal-title":"Proc Natl Acad Sci"},{"key":"2021090815124904200_ref27","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1093\/bib\/bbz098","article-title":"DeepSVM-fold: protein fold recognition by combining support vector machines and pairwise sequence similarity scores generated by deep learning networks","volume":"21","author":"Liu","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090815124904200_ref28","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1093\/bib\/bbz133","article-title":"MotifCNN-fold: protein fold recognition based on fold-specific features extracted by motif-based convolutional neural networks","volume":"21","author":"Li","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090815124904200_ref29","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1093\/bioinformatics\/bty535","article-title":"Compound\u2013protein interaction prediction with end-to-end learning of neural networks for graphs and sequences","volume":"35","author":"Tsubaki","year":"2019","journal-title":"Bioinformatics"},{"key":"2021090815124904200_ref30","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 Bioinforma"},{"key":"2021090815124904200_ref31","doi-asserted-by":"crossref","first-page":"5191","DOI":"10.1093\/bioinformatics\/btz418","article-title":"deepDR: a network-based deep learning approach to in silico drug repositioning","volume":"35","author":"Zeng","year":"2019","journal-title":"Bioinformatics"},{"key":"2021090815124904200_ref32","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1039\/C9SC04336E","article-title":"Target identification among known drugs by deep learning from heterogeneous networks","volume":"11","author":"Zeng","year":"2020","journal-title":"Chem Sci"},{"key":"2021090815124904200_ref33","doi-asserted-by":"crossref","first-page":"2805","DOI":"10.1093\/bioinformatics\/btaa010","article-title":"Network-based prediction of drug\u2013target interactions using an arbitrary-order proximity embedded deep forest","volume":"36","author":"Zeng","year":"2020","journal-title":"Bioinformatics"},{"key":"2021090815124904200_ref34","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":"2021090815124904200_ref35","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1002\/prot.25824","article-title":"Recent developments in deep learning applied to protein structure prediction","volume":"87","author":"Kandathil","year":"2019","journal-title":"Proteins: Structure, Function, and Bioinformatics"},{"key":"2021090815124904200_ref36","doi-asserted-by":"crossref","first-page":"90","DOI":"10.2174\/1574893614666191017104639","article-title":"Protein secondary structure prediction: a review of progress and directions","volume":"15","author":"Smolarczyk","year":"2020","journal-title":"Curr Bioinforma"},{"key":"2021090815124904200_ref37","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa043","article-title":"Application of deep learning methods in biological networks","author":"Jin","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021090815124904200_ref38","doi-asserted-by":"crossref","first-page":"3335","DOI":"10.1016\/j.csbj.2020.10.022","article-title":"Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks","volume":"18","author":"Wang","year":"2020","journal-title":"Comput Struct Biotechnol J"},{"key":"2021090815124904200_ref39","doi-asserted-by":"publisher","DOI":"10.1101\/2020.08.02.233569","article-title":"scGNN: a novel graph neural network framework for single-cell RNA-Seq analyses","volume":"23","author":"Wang","year":"2020","journal-title":"bioRxiv"},{"key":"2021090815124904200_ref40","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.3390\/cells9091938","article-title":"Detecting interactive gene groups for single-cell RNA-Seq data based on co-expression network analysis and subgraph learning","volume":"9","author":"Ye","year":"2020","journal-title":"Cell"},{"key":"2021090815124904200_ref41","article-title":"Robust similarity measure for spectral clustering based on shared Neighbors","volume":"38","author":"Ye","year":"2016","journal-title":"ETRI J"},{"key":"2021090815124904200_ref42","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1186\/s12859-017-1842-2","article-title":"Protein remote homology detection based on bidirectional long short-term memory","volume":"18","author":"Li","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"2021090815124904200_ref43","doi-asserted-by":"crossref","first-page":"46757","DOI":"10.1038\/srep46757","article-title":"Identifying N 6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine","volume":"7","author":"Xing","year":"2017","journal-title":"Sci Rep"},{"key":"2021090815124904200_ref44","first-page":"D135","article-title":"RNALocate: a resource for RNA subcellular localizations","volume":"45","author":"Zhang","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2021090815124904200_ref45","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1093\/bioinformatics\/btw630","article-title":"Pro54DB: a database for experimentally verified sigma-54 promoters","volume":"33","author":"Liang","year":"2017","journal-title":"Bioinformatics"},{"key":"2021090815124904200_ref46","doi-asserted-by":"crossref","first-page":"D325","DOI":"10.1093\/nar\/gkr886","article-title":"ConoServer: updated content, knowledge, and discovery tools in the conopeptide database","volume":"40","author":"Kaas","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2021090815124904200_ref47","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1186\/1471-2164-10-375","article-title":"ArachnoServer: a database of protein toxins from spiders","volume":"10","author":"Wood","year":"2009","journal-title":"BMC Genomics"},{"key":"2021090815124904200_ref48","doi-asserted-by":"crossref","first-page":"D115","DOI":"10.1093\/nar\/gkh131","article-title":"UniProt: the universal protein knowledgebase","volume":"32","author":"Apweiler","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2021090815124904200_ref49","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","article-title":"Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences","volume":"22","author":"Li","year":"2006","journal-title":"Bioinformatics"},{"key":"2021090815124904200_ref50","volume-title":"RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling","author":"Landrum","year":"2013"},{"key":"2021090815124904200_ref51","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"},{"key":"2021090815124904200_ref52","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/j.knosys.2018.10.007","article-title":"Predicting protein structural classes for low-similarity sequences by evaluating different features","volume":"163","author":"Zhu","year":"2019","journal-title":"Knowl-Based Syst"},{"key":"2021090815124904200_ref53","doi-asserted-by":"crossref","first-page":"115","DOI":"10.2174\/1574893613666180209161152","article-title":"Improving self-interacting proteins prediction accuracy using protein evolutionary information and weighed-extreme learning machine","volume":"14","author":"An","year":"2019","journal-title":"Curr Bioinforma"},{"key":"2021090815124904200_ref54","first-page":"4602","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Morris","year":"2019"},{"key":"2021090815124904200_ref55","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun ACM"},{"key":"2021090815124904200_ref56","doi-asserted-by":"crossref","first-page":"233","DOI":"10.2174\/1574893612666170221152848","article-title":"Deep convolutional neural networks for predicting Hydroxyproline in proteins","volume":"12","author":"Long","year":"2017","journal-title":"Curr Bioinforma"},{"key":"2021090815124904200_ref57","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"2021090815124904200_ref58","doi-asserted-by":"crossref","first-page":"29","DOI":"10.3389\/fncom.2020.00029","article-title":"Attention in psychology","volume":"14","author":"Lindsay","year":"2020","journal-title":"Neuroscience, and Machine Learning, Frontiers in Computational Neuroscience"},{"key":"2021090815124904200_ref59","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1093\/bioinformatics\/btz694","article-title":"Identifying enhancer\u2013promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism","volume":"36","author":"Hong","year":"2020","journal-title":"Bioinformatics"},{"key":"2021090815124904200_ref60","doi-asserted-by":"crossref","first-page":"4125","DOI":"10.1021\/acs.jproteome.0c00590","article-title":"iDPPIV-SCM: a sequence-based predictor for identifying and analyzing dipeptidyl peptidase IV (DPP-IV) inhibitory peptides using a scoring card method","volume":"19","author":"Charoenkwan","year":"2020","journal-title":"J Proteome Res"},{"key":"2021090815124904200_ref61","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.ygeno.2020.09.065","article-title":"iAMY-SCM: improved prediction and analysis of amyloid proteins using a scoring card method with propensity scores of dipeptides","volume":"113","author":"Charoenkwan","year":"2021","journal-title":"Genomics"},{"key":"2021090815124904200_ref62","doi-asserted-by":"crossref","first-page":"6666","DOI":"10.1021\/acs.jcim.0c00707","article-title":"iUmami-SCM: a novel sequence-based predictor for prediction and analysis of umami peptides using a scoring card method with propensity scores of dipeptides","volume":"60","author":"Charoenkwan","year":"2020","journal-title":"J Chem Inf Model"},{"key":"2021090815124904200_ref63","doi-asserted-by":"crossref","first-page":"2813","DOI":"10.1016\/j.ygeno.2020.03.019","article-title":"iBitter-SCM: identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides","volume":"112","author":"Charoenkwan","year":"2020","journal-title":"Genomics"},{"key":"2021090815124904200_ref64","first-page":"1","volume-title":"Adaptive Unsupervised Feature Learning for Gene Signature Identification in Non-small-cell Lung Cancer","author":"Ye","year":"2020"},{"key":"2021090815124904200_ref65","first-page":"12512","article-title":"An in silico platform for predicting, screening and designing of antihypertensive peptides","volume":"5","author":"Kumar","year":"2015","journal-title":"LA Rep"},{"key":"2021090815124904200_ref66","first-page":"28","article-title":"TpPred: a tool for hierarchical prediction of transport proteins using cluster of neural networks and sequence derived features","volume":"1","author":"Jain","year":"2014","journal-title":"IJCB"},{"key":"2021090815124904200_ref67","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1002\/prot.1035","article-title":"Prediction of protein cellular attributes using pseudo-amino acid composition, proteins: structure","volume":"43","author":"Chou","year":"2001","journal-title":"Function, and Bioinformatics"},{"key":"2021090815124904200_ref68","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1093\/bioinformatics\/bth466","article-title":"Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes","volume":"21","author":"Chou","year":"2005","journal-title":"Bioinformatics"},{"key":"2021090815124904200_ref69","first-page":"3146","volume-title":"Advances in neural information processing systems","author":"Ke","year":"2017"},{"key":"2021090815124904200_ref70","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"LVD","year":"2008","journal-title":"J Mach Learn Res"},{"key":"2021090815124904200_ref71","doi-asserted-by":"crossref","first-page":"10915","DOI":"10.1073\/pnas.89.22.10915","article-title":"Amino acid substitution matrices from protein blocks","volume":"89","author":"Henikoff","year":"1992","journal-title":"Proc Natl Acad Sci"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/5\/bbab041\/40260952\/bbab041.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/5\/bbab041\/40260952\/bbab041.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T17:43:49Z","timestamp":1697737429000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab041\/6209691"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,5]]},"references-count":71,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,9,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab041","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,9]]},"published":{"date-parts":[[2021,4,5]]},"article-number":"bbab041"}}