{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:05Z","timestamp":1772138045647,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T00:00:00Z","timestamp":1622160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology"},{"name":"HFRI faculty","award":["1725"],"award-info":[{"award-number":["1725"]}]},{"DOI":"10.13039\/501100004343","name":"Stavros Niarchos Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004343","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Protein structural class prediction is one of the most significant problems in bioinformatics, as it has a prominent role in understanding the function and evolution of proteins. Designing a computationally efficient but at the same time accurate prediction method remains a pressing issue, especially for sequences that we cannot obtain a sufficient amount of homologous information from existing protein sequence databases. Several studies demonstrate the potential of utilizing chaos game representation along with time series analysis tools such as recurrence quantification analysis, complex networks, horizontal visibility graphs (HVG) and others. However, the majority of existing works involve a large amount of features and they require an exhaustive, time consuming search of the optimal parameters. To address the aforementioned problems, this work adopts the generalized multidimensional recurrence quantification analysis (GmdRQA) as an efficient tool that enables to process concurrently a multidimensional time series and reduce the number of features. In addition, two data-driven algorithms, namely average mutual information and false nearest neighbors, are utilized to define in a fast yet precise manner the optimal GmdRQA parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The classification accuracy is improved by the combination of GmdRQA with the HVG. Experimental evaluation on a real benchmark dataset demonstrates that our methods achieve similar performance with the state-of-the-art but with a smaller computational cost.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The code to reproduce all the results is available at https:\/\/github.com\/aretiz\/protein_structure_classification\/tree\/main.<\/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\/btab407","type":"journal-article","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T08:46:35Z","timestamp":1622105195000},"page":"1796-1804","source":"Crossref","is-referenced-by-count":8,"title":["Structural classification of proteins based on the computationally efficient recurrence quantification analysis and horizontal visibility graphs"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1289-7463","authenticated-orcid":false,"given":"Michaela Areti","family":"Zervou","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Crete , Heraklion 700 13, Greece"},{"name":"Signal Processing Lab, Institute of Computer Science, Foundation for Research and Technology-Hellas , Heraklion 700 13, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Effrosyni","family":"Doutsi","sequence":"additional","affiliation":[{"name":"Signal Processing Lab, Institute of Computer Science, Foundation for Research and Technology-Hellas , Heraklion 700 13, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pavlos","family":"Pavlidis","sequence":"additional","affiliation":[{"name":"Signal Processing Lab, Institute of Computer Science, Foundation for Research and Technology-Hellas , Heraklion 700 13, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panagiotis","family":"Tsakalides","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Crete , Heraklion 700 13, Greece"},{"name":"Signal Processing Lab, Institute of Computer Science, Foundation for Research and Technology-Hellas , Heraklion 700 13, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,5,28]]},"reference":[{"key":"2023051611463293400_btab407-B1","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1093\/bioinformatics\/17.5.429","article-title":"Analysis of genomic sequences by chaos game representation","volume":"17","author":"Almeida","year":"2001","journal-title":"Bioinformatics"},{"key":"2023051611463293400_btab407-B2","doi-asserted-by":"crossref","first-page":"107164","DOI":"10.1016\/j.compbiolchem.2019.107164","article-title":"Predicting structural class for protein sequences of 40% identity based on features of primary and secondary structure using random forest algorithm","volume":"84","author":"Apurva","year":"2020","journal-title":"Comput. Biol. Chem"},{"key":"2023051611463293400_btab407-B3","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 Struct. Funct. Bioinf"},{"key":"2023051611463293400_btab407-B4","first-page":"208","author":"Dehzangi","year":"2013"},{"key":"2023051611463293400_btab407-B5","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.biochi.2013.09.013","article-title":"A protein structural classes prediction method based on predicted secondary structure and psi-blast profile","volume":"97","author":"Ding","year":"2014","journal-title":"Biochimie"},{"key":"2023051611463293400_btab407-B6","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1209\/0295-5075\/4\/9\/004","article-title":"Recurrence plots of dynamical systems","volume":"4","author":"Eckmann","year":"1987","journal-title":"Europh. Lett"},{"key":"2023051611463293400_btab407-B7","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1103\/PhysRevA.33.1134","article-title":"Independent coordinates for strange attractors from mutual information","volume":"33","author":"Fraser","year":"1986","journal-title":"Phys. Rev. A"},{"key":"2023051611463293400_btab407-B8","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1093\/bioinformatics\/btm274","article-title":"Secondary structure based analysis and classification of biological interfaces: identification of binding motifs in protein\u2013protein interactions","volume":"23","author":"Guharoy","year":"2007","journal-title":"Bioinformatics"},{"key":"2023051611463293400_btab407-B9","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1093\/nar\/18.8.2163","article-title":"Chaos game representation of gene structure","volume":"18","author":"Jeffrey","year":"1990","journal-title":"Nucleic Acids Res"},{"key":"2023051611463293400_btab407-B10","first-page":"531","author":"Jiang","year":"2019"},{"key":"2023051611463293400_btab407-B11","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1006\/jmbi.1999.3091","article-title":"Protein secondary structure prediction based on position-specific scoring matrices","volume":"292","author":"Jones","year":"1999","journal-title":"J. Mol. Biol"},{"key":"2023051611463293400_btab407-B12","volume-title":"Nonlinear Time Series Analysis","author":"Kantz","year":"2004"},{"key":"2023051611463293400_btab407-B13","doi-asserted-by":"crossref","first-page":"3403","DOI":"10.1103\/PhysRevA.45.3403","article-title":"Determining embedding dimension for phase-space reconstruction using a geometrical construction","volume":"45","author":"Kennel","year":"1992","journal-title":"Phys. Rev. A"},{"key":"2023051611463293400_btab407-B14","author":"Kr\u00e4mer","year":"2018"},{"key":"2023051611463293400_btab407-B15","doi-asserted-by":"crossref","first-page":"4972","DOI":"10.1073\/pnas.0709247105","article-title":"From time series to complex networks: the visibility graph","volume":"105","author":"Lacasa","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023051611463293400_btab407-B16","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1038\/261552a0","article-title":"Structural patterns in globular proteins","volume":"261","author":"Levitt","year":"1976","journal-title":"Nature"},{"key":"2023051611463293400_btab407-B17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/370756","article-title":"Prediction of protein structural classes for low-similarity sequences based on consensus sequence and segmented PSSM","volume":"2015","author":"Liang","year":"2015","journal-title":"Comput. Math. Methods Med"},{"key":"2023051611463293400_btab407-B18","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.jtbi.2010.09.007","article-title":"A high-accuracy protein structural class prediction algorithm using predicted secondary structural information","volume":"267","author":"Liu","year":"2010","journal-title":"J. Theor. Biol"},{"key":"2023051611463293400_btab407-B19","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1016\/j.biochi.2010.06.013","article-title":"Prediction of protein structural class for low-similarity sequences using support vector machine and psi-blast profile","volume":"92","author":"Liu","year":"2010","journal-title":"Biochimie"},{"key":"2023051611463293400_btab407-B20","doi-asserted-by":"crossref","first-page":"15","DOI":"10.3390\/ijms17010015","article-title":"Prediction of protein structural class based on gapped-dipeptides and a recursive feature selection approach","volume":"17","author":"Liu","year":"2015","journal-title":"Int. J. Mol. Sci"},{"key":"2023051611463293400_btab407-B21","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1093\/bioinformatics\/btz493","article-title":"Deep learning on chaos game representation for proteins","volume":"36","author":"L\u00f6chel","year":"2020","journal-title":"Bioinformatics"},{"key":"2023051611463293400_btab407-B22","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.physrep.2006.11.001","article-title":"Recurrence plots for the analysis of complex systems","volume":"438","author":"Marwan","year":"2007","journal-title":"Phys. Rep"},{"key":"2023051611463293400_btab407-B23","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1093\/oxfordjournals.jbchem.a135454","article-title":"The folding type of a protein is relevant to the amino acid composition","volume":"99","author":"Nakashima","year":"1986","journal-title":"J. Biochem"},{"key":"2023051611463293400_btab407-B24","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1126\/science.1095920","article-title":"Protein kinase inhibitors: insights into drug design from structure","volume":"303","author":"Noble","year":"2004","journal-title":"Science"},{"key":"2023051611463293400_btab407-B25","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.jtbi.2016.06.018","article-title":"Predicting protein structural classes based on complex networks and recurrence analysis","volume":"404","author":"Olyaee","year":"2016","journal-title":"J. Theor. Biol"},{"key":"2023051611463293400_btab407-B26","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1016\/S0969-2126(97)00260-8","article-title":"Cath\u2014a hierarchic classification of protein domain structures","volume":"5","author":"Orengo","year":"1997","journal-title":"Structure"},{"key":"2023051611463293400_btab407-B27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13040-019-0196-x","article-title":"Encodings and models for antimicrobial peptide classification for multi-resistant pathogens","volume":"12","author":"Sp\u00e4nig","year":"2019","journal-title":"BioData Min"},{"key":"2023051611463293400_btab407-B28","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.3389\/fpsyg.2018.01679","article-title":"Calculation of average mutual information (AMI) and false-nearest neighbors (FNN) for the estimation of embedding parameters of multidimensional time series in matlab","volume":"9","author":"Wallot","year":"2018","journal-title":"Front. Psychol"},{"key":"2023051611463293400_btab407-B29","doi-asserted-by":"crossref","first-page":"1835","DOI":"10.3389\/fpsyg.2016.01835","article-title":"Multidimensional recurrence quantification analysis (MDRQA) for the analysis of multidimensional time-series: a software implementation in matlab and its application to group-level data in joint action","volume":"7","author":"Wallot","year":"2016","journal-title":"Front. Psychol"},{"key":"2023051611463293400_btab407-B30","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.gene.2014.10.037","article-title":"Prediction of protein structural classes for low-similarity sequences using reduced PSSM and position-based secondary structural features","volume":"554","author":"Wang","year":"2015","journal-title":"Gene"},{"key":"2023051611463293400_btab407-B31","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.physa.2016.11.058","article-title":"Multiscale recurrence quantification analysis of order recurrence plots","volume":"469","author":"Xu","year":"2017","journal-title":"Physica A"},{"key":"2023051611463293400_btab407-B32","first-page":"665","author":"Yang","year":"2008"},{"key":"2023051611463293400_btab407-B33","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.jtbi.2008.12.027","article-title":"Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation","volume":"257","author":"Yang","year":"2009","journal-title":"J. Theor. Biol"},{"key":"2023051611463293400_btab407-B34","doi-asserted-by":"crossref","first-page":"S9","DOI":"10.1186\/1471-2105-11-S1-S9","article-title":"Prediction of protein structural classes for low-homology sequences based on predicted secondary structure","volume":"11","author":"Yang","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2023051611463293400_btab407-B35","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.jmgm.2017.07.012","article-title":"Prediction of protein structural class for low-similarity sequences using chou\u2019s pseudo amino acid composition and wavelet denoising","volume":"76","author":"Yu","year":"2017","journal-title":"J. Mol. Graph. Modell"},{"key":"2023051611463293400_btab407-B36","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.compbiolchem.2018.06.007","article-title":"A novel feature selection method to predict protein structural class","volume":"76","author":"Yuan","year":"2018","journal-title":"Comput. Biol. Chem"},{"key":"2023051611463293400_btab407-B37","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/0375-9601(92)90426-M","article-title":"Embeddings and delays as derived from quantification of recurrence plots","volume":"171","author":"Zbilut","year":"1992","journal-title":"Phys. Lett. A"},{"key":"2023051611463293400_btab407-B38","first-page":"770","author":"Zervou","year":"2019"},{"key":"2023051611463293400_btab407-B39","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1080\/07391102.2011.672627","article-title":"Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM","volume":"29","author":"Zhang","year":"2012","journal-title":"J. Biomol. Struct. Dyn"},{"key":"2023051611463293400_btab407-B40","doi-asserted-by":"crossref","first-page":"67","DOI":"10.3844\/ajbbsp.2018.67.75","article-title":"Low-homology protein structural class prediction from secondary structure based on visibility and horizontal visibility network","volume":"14","author":"Zhao","year":"2018","journal-title":"Am. J. Biochem. Biotechnol"},{"key":"2023051611463293400_btab407-B41","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":"Knowledge Based Syst"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab407\/38361026\/btab407.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/13\/1796\/50340167\/btab407.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/13\/1796\/50340167\/btab407.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T07:48:49Z","timestamp":1684223329000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/13\/1796\/6287612"}},"subtitle":[],"editor":[{"given":"Pier Luigi","family":"Martelli","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,5,28]]},"references-count":41,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2021,7,27]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab407","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.10.23.350736","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":[[2021,7,1]]},"published":{"date-parts":[[2021,5,28]]}}}