{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:37:37Z","timestamp":1761395857943,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T00:00:00Z","timestamp":1602028800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T00:00:00Z","timestamp":1602028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872220","61873001"],"award-info":[{"award-number":["61872220","61873001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the <jats:italic>L<\/jats:italic><jats:sub>2,1<\/jats:sub>-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-020-03790-1","type":"journal-article","created":{"date-parts":[[2020,10,7]],"date-time":"2020-10-07T18:03:01Z","timestamp":1602093781000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification"],"prefix":"10.1186","volume":"21","author":[{"given":"Liang-Rui","family":"Ren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying-Lian","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6104-2149","authenticated-orcid":false,"given":"Jin-Xing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junliang","family":"Shang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Hou","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,7]]},"reference":[{"issue":"6","key":"3790_CR1","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/S0893-6080(05)80131-5","volume":"6","author":"M Leshno","year":"1993","unstructured":"Leshno M, Lin VY, Pinkus A, Schocken S. Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw. 1993;6(6):861\u201367.","journal-title":"Neural Netw"},{"key":"3790_CR2","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat No 04CH37541): 2004. IEEE, pp. 985\u2013990."},{"issue":"1\u20133","key":"3790_CR3","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK. Extreme learning machine: theory and applications. Neurocomputing. 2006;70(1\u20133):489\u2013501.","journal-title":"Neurocomputing"},{"issue":"2","key":"3790_CR4","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","volume":"2","author":"GB Huang","year":"2011","unstructured":"Huang GB, Wang DH, Lan Y. Extreme learning machines: a survey. Int J Mach Learn Cybernet. 2011;2(2):107\u201322.","journal-title":"Int J Mach Learn Cybernet"},{"issue":"2","key":"3790_CR5","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"GB Huang","year":"2012","unstructured":"Huang GB, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybernet Part B. 2012;42(2):513\u2013529.","journal-title":"IEEE Trans Syst Man Cybernet Part B"},{"issue":"4","key":"3790_CR6","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1109\/TNN.2006.875977","volume":"17","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Chen L, Siew CK. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw Learn Syst. 2006;17(4):879\u201392.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"3790_CR7","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1007\/s12559-014-9255-2","volume":"6","author":"G-B Huang","year":"2014","unstructured":"Huang G-B. An insight into extreme learning machines: random neurons, random features and kernels. Cognit Comput. 2014;6(3):376\u201390.","journal-title":"Cognit Comput"},{"issue":"3","key":"3790_CR8","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s12559-015-9333-0","volume":"7","author":"GB Huang","year":"2015","unstructured":"Huang GB. What are extreme learning machines? Filling the gap between Frank Rosenblatt\u2019s Dream and John von Neumann\u2019s Puzzle. Cognit Comput. 2015;7(3):263\u201378.","journal-title":"Cognit Comput"},{"key":"3790_CR9","doi-asserted-by":"publisher","first-page":"6575","DOI":"10.1109\/ACCESS.2018.2887260","volume":"7","author":"R Li","year":"2018","unstructured":"Li R, Wang X, Lei L, Song Y. L2,1-norm based loss function and regularization extreme learning machine. IEEE Access. 2018;7:6575\u201386.","journal-title":"IEEE Access"},{"key":"3790_CR10","first-page":"15","volume-title":"Neural networks and back propagation algorithm","author":"M Cilimkovic","year":"2015","unstructured":"Cilimkovic M. Neural networks and back propagation algorithm. Dublin: Institute of Technology Blanchardstown; 2015. p. 15."},{"issue":"6","key":"3790_CR11","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1109\/TNN.2006.880360","volume":"17","author":"Z Man","year":"2006","unstructured":"Man Z, Wu HR, Liu S, Yu X. A new adaptive backpropagation algorithm based on Lyapunov stability theory for neural networks. IEEE Trans Neural Networks. 2006;17(6):1580\u201391.","journal-title":"IEEE Trans Neural Networks"},{"issue":"3\u20134","key":"3790_CR12","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/s00521-013-1512-x","volume":"25","author":"H Lu","year":"2014","unstructured":"Lu H, Zheng E, Lu Y, Ma X, Liu J. ELM-based gene expression classification with misclassification cost. Neural Comput Appl. 2014;25(3\u20134):525\u201331.","journal-title":"Neural Comput Appl"},{"issue":"12","key":"3790_CR13","doi-asserted-by":"publisher","first-page":"2405","DOI":"10.1109\/TCYB.2014.2307349","volume":"44","author":"G Huang","year":"2014","unstructured":"Huang G, Song S, Gupta JN, Wu C. Semi-supervised and unsupervised extreme learning machines. IEEE Trans Cybernet. 2014;44(12):2405\u201317.","journal-title":"IEEE Trans Cybernet"},{"issue":"C","key":"3790_CR14","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","volume":"61","author":"G Huang","year":"2015","unstructured":"Huang G, Huang GB, Song S, You K. Trends in extreme learning machines: a review. Neural Netw. 2015;61(C):32\u201348.","journal-title":"Neural Netw"},{"key":"3790_CR15","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neucom.2012.02.042","volume":"102","author":"F Cao","year":"2013","unstructured":"Cao F, Liu B, Park DS. Image classification based on effective extreme learning machine. Neurocomputing. 2013;102:90\u20137.","journal-title":"Neurocomputing"},{"key":"3790_CR16","first-page":"6809","volume":"5","author":"U Ergul","year":"2019","unstructured":"Ergul U, Bilgin G. MCK-ELM: multiple composite kernel extreme learning machine for hyperspectral images. Neural Comput Appl. 2020, 32(11):6809\u201319","journal-title":"Neural Comput Appl"},{"key":"3790_CR17","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.neucom.2016.04.069","volume":"261","author":"M Jiang","year":"2017","unstructured":"Jiang M, Pan Z, Li N. Multi-label text categorization using L21-norm minimization extreme learning machine. Neurocomputing. 2017;261:4\u201310.","journal-title":"Neurocomputing"},{"issue":"3","key":"3790_CR18","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1109\/TCYB.2018.2889376","volume":"50","author":"C Deng","year":"2019","unstructured":"Deng C, Wang S, Bovik AC, Huang G-B, Zhao B. Blind noisy image quality assessment using sub-band kurtosis. IEEE Trans Cybernet. 2019;50(3):1146\u201356.","journal-title":"IEEE Trans Cybernet"},{"issue":"4","key":"3790_CR19","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1109\/TCYB.2016.2533424","volume":"47","author":"Z Huang","year":"2016","unstructured":"Huang Z, Yu Y, Gu J, Liu H. An efficient method for traffic sign recognition based on extreme learning machine. IEEE Trans Cybernet. 2016;47(4):920\u201333.","journal-title":"IEEE Trans Cybernet"},{"key":"3790_CR20","unstructured":"Liu W, Pokharel PP, Principe JC. Correntropy: a localized similarity measure. In: The 2006 IEEE international joint conference on neural network proceedings; 2006. IEEE, pp. 4919\u20134924."},{"key":"3790_CR21","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neucom.2018.05.100","volume":"313","author":"Z Ren","year":"2018","unstructured":"Ren Z, Yang L. Correntropy-based robust extreme learning machine for classification. Neurocomputing. 2018;313:74\u201384.","journal-title":"Neurocomputing"},{"issue":"1","key":"3790_CR22","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.patcog.2013.07.017","volume":"47","author":"A Singh","year":"2014","unstructured":"Singh A, Pokharel R, Principe J. The C-loss function for pattern classification. Pattern Recognit. 2014;47(1):441\u201353.","journal-title":"Pattern Recognit"},{"issue":"3","key":"3790_CR23","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1109\/TNNLS.2016.2637351","volume":"29","author":"G Xu","year":"2016","unstructured":"Xu G, Hu B-G, Principe JC. Robust C-loss kernel classifiers. IEEE Trans Neural Netw Learn Syst. 2016;29(3):510\u201322.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3790_CR24","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.ast.2019.04.023","volume":"89","author":"Y-P Zhao","year":"2019","unstructured":"Zhao Y-P, Tan J-F, Wang J-J, Yang Z. C-loss based extreme learning machine for estimating power of small-scale turbojet engine. Aerosp Sci Technol. 2019;89:407\u201319.","journal-title":"Aerosp Sci Technol"},{"key":"3790_CR25","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.patcog.2018.07.011","volume":"84","author":"C Liangjun","year":"2018","unstructured":"Liangjun C, Honeine P, Hua Q, Jihong Z, Xia S. Correntropy-based robust multilayer extreme learning machines. Pattern Recognit. 2018;84:357\u201370.","journal-title":"Pattern Recognit"},{"issue":"5","key":"3790_CR26","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1109\/TIP.2005.864173","volume":"15","author":"M Allain","year":"2006","unstructured":"Allain M, Idier J, Goussard Y. On global and local convergence of half-quadratic algorithms. IEEE Trans Image Process. 2006;15(5):1130\u201342.","journal-title":"IEEE Trans Image Process"},{"key":"3790_CR27","doi-asserted-by":"crossref","unstructured":"Sindhwani V, Niyogi P, Belkin M. Beyond the point cloud: from transductive to semi-supervised learning. In: Proceedings of the 22nd international conference on machine learning; 2005, pp. 824\u2013831.","DOI":"10.1145\/1102351.1102455"},{"key":"3790_CR28","doi-asserted-by":"crossref","unstructured":"Sindhwani V, Rosenberg DS. An RKHS for multi-view learning and manifold co-regularization. In: Proceedings of the 25th International Conference on Machine Learning; 2008, pp. 976\u2013983.","DOI":"10.1145\/1390156.1390279"},{"issue":"3","key":"3790_CR29","first-page":"1149","volume":"12","author":"S Melacci","year":"2011","unstructured":"Melacci S, Belkin M. Laplacian support vector machines trained in the primal. J Mach Learn Res. 2011;12(3):1149\u201384.","journal-title":"J Mach Learn Res"},{"key":"3790_CR30","doi-asserted-by":"crossref","unstructured":"Lekamalage CKL, Liu T, Yang Y, Lin Z, Huang G-B. Extreme learning machine for clustering. In: Proceedings of ELM-2014 Volume 1. Springer; 2015: 435\u2013444.","DOI":"10.1007\/978-3-319-14063-6_36"},{"key":"3790_CR31","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.neucom.2017.01.115","volume":"277","author":"T Liu","year":"2018","unstructured":"Liu T, Lekamalage CKL, Huang G-B, Lin Z. Extreme learning machine for joint embedding and clustering. Neurocomputing. 2018;277:78\u201388.","journal-title":"Neurocomputing"},{"issue":"5439","key":"3790_CR32","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286(5439):531\u201337.","journal-title":"Science"},{"key":"3790_CR33","doi-asserted-by":"publisher","first-page":"7081674","DOI":"10.1155\/2019\/7081674","volume":"2019","author":"Y-J Hao","year":"2019","unstructured":"Hao Y-J, Gao Y-L, Hou M-X, Dai L-Y, Liu J-X. Hypergraph regularized discriminative nonnegative matrix factorization on sample classification and co-differentially expressed gene selection. Complexity. 2019;2019:7081674.","journal-title":"Complexity"},{"key":"3790_CR34","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.neucom.2015.03.113","volume":"174","author":"S Zhou","year":"2016","unstructured":"Zhou S, Liu X, Liu Q, Wang S, Zhu C, Yin J. Random Fourier extreme learning machine with L2,1-norm regularization. Neurocomputing. 2016;174:143\u201353.","journal-title":"Neurocomputing"},{"key":"3790_CR35","doi-asserted-by":"crossref","unstructured":"Lu Y, Gao Y-L, Liu J-X, Wen C-G, Wang Y-X, Yu J. Characteristic gene selection via L 2, 1-norm sparse principal component analysis. In: 2016 IEEE international conference on bioinformatics and biomedicine (BIBM): 2016. IEEE, pp. 1828\u20131833.","DOI":"10.1109\/BIBM.2016.7822796"},{"key":"3790_CR36","doi-asserted-by":"crossref","unstructured":"Ding C, Zhou D, He X, Zha H. R 1-PCA: rotational invariant L 1-norm principal component analysis for robust subspace factorization. In: Proceedings of the 23rd international conference on machine learning: 2006. ACM, pp. 281\u2013288.","DOI":"10.1145\/1143844.1143880"},{"key":"3790_CR37","unstructured":"Yang Y, Shen HT, Ma Z, Huang Z, Zhou X. L21-norm regularized discriminative feature selection for unsupervised learning. In: International joint conference on artificial intelligence; 2011."},{"key":"3790_CR38","unstructured":"Nie F, Huang H, Cai X, Ding CH. Efficient and robust feature selection via joint \u21132, 1-norms minimization. In: Advances in neural information processing systems, 2010; pp. 1813\u20131821."},{"issue":"1","key":"3790_CR39","first-page":"2399","volume":"7","author":"M Belkin","year":"2006","unstructured":"Belkin M, Niyogi P, Sindhwani V. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res. 2006;7(1):2399\u2013434.","journal-title":"J Mach Learn Res"},{"key":"3790_CR40","doi-asserted-by":"crossref","unstructured":"Yu N, Liu J-X, Gao Y-L, Zheng C-H, Wang J, Wu M-J: Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes. In: 2017 IEEE international conference on bioinformatics and biomedicine (BIBM); 2017. IEEE, pp. 1752\u20131756.","DOI":"10.1109\/BIBM.2017.8217925"},{"key":"3790_CR41","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neucom.2012.12.063","volume":"128","author":"Q He","year":"2014","unstructured":"He Q, Jin X, Du C, Zhuang F, Shi Z. Clustering in extreme learning machine feature space. Neurocomputing. 2014;128:88\u201395.","journal-title":"Neurocomputing"},{"issue":"12","key":"3790_CR42","doi-asserted-by":"publisher","first-page":"586","DOI":"10.3390\/genes9120586","volume":"9","author":"N Yu","year":"2018","unstructured":"Yu N, Gao Y-L, Liu J-X, Shang J, Zhu R, Dai L-Y. Co-differential gene selection and clustering based on graph regularized multi-view NMF in cancer genomic data. Genes. 2018;9(12):586.","journal-title":"Genes"},{"issue":"2","key":"3790_CR43","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1039\/C8MO00244D","volume":"15","author":"M-M Gao","year":"2019","unstructured":"Gao M-M, Cui Z, Gao Y-L, Liu J-X, Zheng C-H. Dual-network sparse graph regularized matrix factorization for predicting miRNA\u2013disease associations. Mol Omics. 2019;15(2):130\u201337.","journal-title":"Mol Omics"},{"key":"3790_CR44","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.neucom.2011.12.045","volume":"102","author":"P Horata","year":"2013","unstructured":"Horata P, Chiewchanwattana S, Sunat K. Robust extreme learning machine. Neurocomputing. 2013;102:31\u201344.","journal-title":"Neurocomputing"},{"key":"3790_CR45","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.neucom.2013.12.065","volume":"149","author":"Y Peng","year":"2015","unstructured":"Peng Y, Wang S, Long X, Lu B-L. Discriminative graph regularized extreme learning machine and its application to face recognition. Neurocomputing. 2015;149:340\u201353.","journal-title":"Neurocomputing"},{"key":"3790_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2015.06.002","volume":"70","author":"G Huang","year":"2015","unstructured":"Huang G, Liu T, Yang Y, Lin Z, Song S, Wu C. Discriminative clustering via extreme learning machine. Neural Netw. 2015;70:1\u20138.","journal-title":"Neural Netw"},{"issue":"11","key":"3790_CR47","doi-asserted-by":"publisher","first-page":"3545","DOI":"10.1007\/s00500-018-3109-x","volume":"22","author":"Y Yi","year":"2018","unstructured":"Yi Y, Qiao S, Zhou W, Zheng C, Liu Q, Wang J. Adaptive multiple graph regularized semi-supervised extreme learning machine. Soft Comput. 2018;22(11):3545\u201362.","journal-title":"Soft Comput"},{"key":"3790_CR48","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex optimization","author":"S Boyd","year":"2004","unstructured":"Boyd S, Vandenberghe L. Convex optimization. Cambridge: Cambridge University Press; 2004."},{"issue":"2","key":"3790_CR49","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/TPAMI.2013.102","volume":"36","author":"R He","year":"2013","unstructured":"He R, Zheng W-S, Tan T, Sun Z. Half-quadratic-based iterative minimization for robust sparse representation. IEEE Trans Pattern Anal Mach Intell. 2013;36(2):261\u201375.","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03790-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-020-03790-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03790-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T23:32:21Z","timestamp":1633563141000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-03790-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,7]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["3790"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-03790-1","relation":{},"ISSN":["1471-2105"],"issn-type":[{"type":"electronic","value":"1471-2105"}],"subject":[],"published":{"date-parts":[[2020,10,7]]},"assertion":[{"value":"25 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"445"}}