{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:00:11Z","timestamp":1760122811550},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"S19","license":[{"start":{"date-parts":[[2018,12,1]],"date-time":"2018-12-01T00:00:00Z","timestamp":1543622400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1186\/s12859-018-2517-3","type":"journal-article","created":{"date-parts":[[2018,12,31]],"date-time":"2018-12-31T03:43:12Z","timestamp":1546227792000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["In silico design of MHC class I high binding affinity peptides through motifs activation map"],"prefix":"10.1186","volume":"19","author":[{"given":"Zhoujian","family":"Xiao","sequence":"first","affiliation":[]},{"given":"Yuwei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Runsheng","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xiaosen","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Ziwei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shuaicheng","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,12,31]]},"reference":[{"key":"2517_CR1","first-page":"23","volume":"6","author":"JP Pandey","year":"2007","unstructured":"Pandey JP. Major histocompatibility complex. Med Immunol. 2007; 6:23\u201334.","journal-title":"Med Immunol"},{"issue":"5174","key":"2517_CR2","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1126\/science.8052850","volume":"265","author":"M Corr","year":"1994","unstructured":"Corr M, Slanetz AE, Boyd LF, Jelonek MT, Khilko S, Al-Ramadi BK, Kim YS, Maher SE, Bothwell A, Margulies DH. T cell receptor-mhc class i peptide interactions: affinity, kinetics, and specificity. Science. 1994; 265(5174):946\u20139.","journal-title":"Science"},{"issue":"11","key":"2517_CR3","doi-asserted-by":"publisher","first-page":"2594","DOI":"10.1007\/s11095-016-2029-7","volume":"33","author":"S Ekins","year":"2016","unstructured":"Ekins S. The next era: Deep learning in pharmaceutical research. Pharm Res. 2016; 33(11):2594\u2013603.","journal-title":"Pharm Res"},{"issue":"11","key":"2517_CR4","doi-asserted-by":"publisher","first-page":"1003889","DOI":"10.1371\/journal.pcbi.1003889","volume":"10","author":"MJ Skwark","year":"2014","unstructured":"Skwark MJ, Raimondi D, Michel M, Elofsson A. Improved contact predictions using the recognition of protein like contact patterns. PLoS Comput Biol. 2014; 10(11):1003889.","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"2517_CR5","doi-asserted-by":"publisher","first-page":"15264","DOI":"10.1038\/s41598-018-33654-x","volume":"8","author":"J Zheng","year":"2018","unstructured":"Zheng J, Zhang X, Zhao X, Tong X, Hong X, Xie J, Liu S. Deep-rbppred: Predicting rna binding proteins in the proteome scale based on deep learning. Sci Rep. 2018; 8(1):15264.","journal-title":"Sci Rep"},{"issue":"11","key":"2517_CR6","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1093\/nar\/gkw226","volume":"44","author":"D Quang","year":"2016","unstructured":"Quang D, Xie X. Danq: a hybrid convolutional and recurrent deep neural network for quantifying the function of dna sequences. Nucleic Acids Res. 2016; 44(11):107.","journal-title":"Nucleic Acids Res"},{"issue":"7","key":"2517_CR7","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1101\/gr.200535.115","volume":"26","author":"DR Kelley","year":"2016","unstructured":"Kelley DR, Snoek J, Rinn JL. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 2016; 26(7):990\u20139.","journal-title":"Genome Res"},{"issue":"10","key":"2517_CR8","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1038\/nmeth.3547","volume":"12","author":"J Zhou","year":"2015","unstructured":"Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning\u2013based sequence model. Nat Methods. 2015; 12(10):931.","journal-title":"Nat Methods"},{"key":"2517_CR9","unstructured":"Lanchantin J, Singh R, Lin Z, Qi Y. Deep motif: Visualizing genomic sequence classifications. arXiv preprint arXiv:1605.01133. 2016."},{"issue":"17","key":"2517_CR10","doi-asserted-by":"publisher","first-page":"2658","DOI":"10.1093\/bioinformatics\/btx264","volume":"33","author":"YS Vang","year":"2017","unstructured":"Vang YS, Xie X. Hla class i binding prediction via convolutional neural networks. Bioinformatics. 2017; 33(17):2658\u201365.","journal-title":"Bioinformatics"},{"key":"2517_CR11","unstructured":"Bhattacharya R, Tokheim C, Sivakumar A, Guthrie VB, Anagnostou V, Velculescu VE, Karchin R. Prediction of peptide binding to mhc class i proteins in the age of deep learning. bioRxiv. 2017. \n                    https:\/\/doi.org\/10.1101\/154757\n                    \n                  . \n                    http:\/\/arxiv.org\/abs\/https:\/\/www.biorxiv.org\/content\/early\/2017\/06\/23\/154757.full.pdf\n                    \n                  ."},{"issue":"1","key":"2517_CR12","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.cels.2018.05.014","volume":"7","author":"T O\u2019Donnell","year":"2018","unstructured":"O\u2019Donnell T, Rubinsteyn A, Bonsack M, Riemer A, Hammerbacher J. Mhcflurry: open-source class i mhc binding affinity prediction. Cell Syst. 2018; 7(1):129\u201332.","journal-title":"Cell Syst"},{"issue":"9","key":"2517_CR13","doi-asserted-by":"publisher","first-page":"3360","DOI":"10.4049\/jimmunol.1700893","volume":"199","author":"V Jurtz","year":"2017","unstructured":"Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M. Netmhcpan-4.0: Improved peptide\u2013mhc class i interaction predictions integrating eluted ligand and peptide binding affinity data. J Immunol. 2017; 199(9):3360\u20138.","journal-title":"J Immunol"},{"issue":"8","key":"2517_CR14","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1371\/journal.pone.0000796","volume":"2","author":"M Nielsen","year":"2007","unstructured":"Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, R\u00f8der G, Peters B, Sette A, Lund O, et al.Netmhcpan, a method for quantitative predictions of peptide binding to any hla-a and-b locus protein of known sequence. PloS ONE. 2007; 2(8):796.","journal-title":"PloS ONE"},{"issue":"9","key":"2517_CR15","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1016\/S0198-8859(02)00432-9","volume":"63","author":"PA Reche","year":"2002","unstructured":"Reche PA, Glutting J-P, Reinherz EL. Prediction of mhc class i binding peptides using profile motifs. Hum Immunol. 2002; 63(9):701\u20139.","journal-title":"Hum Immunol"},{"issue":"5","key":"2517_CR16","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1110\/ps.0239403","volume":"12","author":"M Nielsen","year":"2003","unstructured":"Nielsen M, Lundegaard C, Worning P, Lauem\u00f8ller SL, Lamberth K, Buus S, Brunak S, Lund O. Reliable prediction of t-cell epitopes using neural networks with novel sequence representations. Protein Sci. 2003; 12(5):1007\u201317.","journal-title":"Protein Sci"},{"issue":"1","key":"2517_CR17","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1186\/1471-2105-6-132","volume":"6","author":"B Peters","year":"2005","unstructured":"Peters B, Sette A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics. 2005; 6(1):132.","journal-title":"BMC Bioinformatics"},{"issue":"10","key":"2517_CR18","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s00251-013-0720-y","volume":"65","author":"E Karosiene","year":"2013","unstructured":"Karosiene E, Rasmussen M, Blicher T, Lund O, Buus S, Nielsen M. Netmhciipan-3. 0, a common pan-specific mhc class ii prediction method including all three human mhc class ii isotypes, hla-dr, hla-dp and hla-dq. Immunogenetics. 2013; 65(10):711\u201324.","journal-title":"Immunogenetics"},{"key":"2517_CR19","doi-asserted-by":"crossref","unstructured":"Mazzaferro C. Predicting protein binding affinity with word embeddings and recurrent neural networks. bioRxiv. 2017;:128223. \n                    https:\/\/www.biorxiv.org\/content\/early\/2017\/04\/18\/128223.abstract\n                    \n                  .","DOI":"10.1101\/128223"},{"issue":"1","key":"2517_CR20","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1186\/s12859-017-1997-x","volume":"18","author":"Y Han","year":"2017","unstructured":"Han Y, Kim D. Deep convolutional neural networks for pan-specific peptide-mhc class i binding prediction. BMC Bioinformatics. 2017; 18(1):585.","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"2517_CR21","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1021\/acs.jcim.6b00740","volume":"57","author":"M Ragoza","year":"2017","unstructured":"Ragoza M, Hochuli J, Idrobo E, Sunseri J, Koes DR. Protein\u2013ligand scoring with convolutional neural networks. J Chem Inf Model. 2017; 57(4):942\u201357.","journal-title":"J Chem Inf Model"},{"issue":"1","key":"2517_CR22","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/minf.201501008","volume":"35","author":"E Gawehn","year":"2016","unstructured":"Gawehn E, Hiss JA, Schneider G. Deep learning in drug discovery. Mol Inform. 2016; 35(1):3\u201314.","journal-title":"Mol Inform"},{"issue":"7","key":"2517_CR23","doi-asserted-by":"publisher","first-page":"10883","DOI":"10.18632\/oncotarget.14073","volume":"8","author":"A Kadurin","year":"2017","unstructured":"Kadurin A, Aliper A, Kazennov A, Mamoshina P, Vanhaelen Q, Khrabrov K, Zhavoronkov A. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget. 2017; 8(7):10883.","journal-title":"Oncotarget"},{"issue":"9","key":"2517_CR24","doi-asserted-by":"publisher","first-page":"3098","DOI":"10.1021\/acs.molpharmaceut.7b00346","volume":"14","author":"A Kadurin","year":"2017","unstructured":"Kadurin A, Nikolenko S, Khrabrov K, Aliper A, Zhavoronkov A. drugan: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol Pharm. 2017; 14(9):3098\u2013104.","journal-title":"Mol Pharm"},{"issue":"1","key":"2517_CR25","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","volume":"4","author":"MH Segler","year":"2017","unstructured":"Segler MH, Kogej T, Tyrchan C, Waller MP. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Central Sci. 2017; 4(1):120\u201331.","journal-title":"ACS Central Sci"},{"issue":"2","key":"2517_CR26","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1021\/ic102031h","volume":"50","author":"G Hautier","year":"2010","unstructured":"Hautier G, Fischer C, Ehrlacher V, Jain A, Ceder G. Data mined ionic substitutions for the discovery of new compounds. Inorg Chem. 2010; 50(2):656\u201363.","journal-title":"Inorg Chem"},{"key":"2517_CR27","unstructured":"Schwaller P, Gaudin T, Lanyi D, Bekas C, Laino T. found in translation: Predicting outcome of complex organic chemistry reactions using neural sequence-to-sequence models. arXiv preprint arXiv:1711.04810. 2017. \n                    https:\/\/arxiv.org\/abs\/1711.04810\n                    \n                  ."},{"issue":"10","key":"2517_CR28","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1021\/acscentsci.7b00303","volume":"3","author":"B Liu","year":"2017","unstructured":"Liu B, Ramsundar B, Kawthekar P, Shi J, Gomes J, Luu Nguyen Q, Ho S, Sloane J, Wender P, Pande V. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Central Sci. 2017; 3(10):1103\u201313.","journal-title":"ACS Central Sci"},{"issue":"W1","key":"2517_CR29","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1093\/nar\/gkx276","volume":"45","author":"M Nielsen","year":"2017","unstructured":"Nielsen M, Andreatta M. Nnalign: a platform to construct and evaluate artificial neural network models of receptor\u2013ligand interactions. Nucleic Acids Res. 2017; 45(W1):344\u20139.","journal-title":"Nucleic Acids Res"},{"key":"2517_CR30","unstructured":"Shrikumar A, Greenside P, Kundaje A. Learning important features through propagating activation differences. arXiv preprint arXiv:1704.02685. 2017. \n                    https:\/\/arxiv.org\/abs\/1704.02685\n                    \n                  ."},{"key":"2517_CR31","doi-asserted-by":"publisher","unstructured":"Alvarez B, Barra C, Nielsen M, Andreatta M. Computational tools for the identification and interpretation of sequence motifs in immunopeptidomes. Proteomics. 2018; 18(12):1700252. \n                    https:\/\/doi.org\/10.1002\/pmic.201700252\n                    \n                  .","DOI":"10.1002\/pmic.201700252"},{"key":"2517_CR32","unstructured":"Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. 2013. \n                    https:\/\/arxiv.org\/abs\/1301.3781\n                    \n                  ."},{"key":"2517_CR33","volume-title":"Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS\u201913","author":"T Mikolov","year":"2013","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS\u201913. USA: Curran Associates Inc.: 2013. p. 3111\u201319."},{"key":"2517_CR34","unstructured":"Le Q, Mikolov T. Distributed representations of sentences and documents. In: International Conference on Machine Learning. JMLR: 2014. p. 1188\u201396."},{"key":"2517_CR35","doi-asserted-by":"crossref","unstructured":"Neelakantan A, Shankar J, Passos A, McCallum A. Efficient non-parametric estimation of multiple embeddings per word in vector space. arXiv preprint arXiv:1504.06654. 2015. \n                    https:\/\/arxiv.org\/abs\/1504.06654\n                    \n                  .","DOI":"10.3115\/v1\/D14-1113"},{"issue":"11","key":"2517_CR36","doi-asserted-by":"publisher","first-page":"0141287","DOI":"10.1371\/journal.pone.0141287","volume":"10","author":"E Asgari","year":"2015","unstructured":"Asgari E, Mofrad MR. Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS ONE. 2015; 10(11):0141287.","journal-title":"PloS ONE"},{"issue":"6324","key":"2517_CR37","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1038\/351290a0","volume":"351","author":"K Falk","year":"1991","unstructured":"Falk K, R\u00f6tzschke O, Stevanovi\u00e9 S, Jung G, Rammensee H-G. Allele-specific motifs revealed by sequencing of self-peptides eluted from mhc molecules. Nature. 1991; 351(6324):290.","journal-title":"Nature"},{"key":"2517_CR38","volume-title":"Seminars in Immunology, Vol. 5","author":"K Falk","year":"1993","unstructured":"Falk K, R\u00f6tzschke O. Consensus motifs and peptide ligands of mhc class i molecules. In: Seminars in Immunology, Vol. 5. Amsterdam: Elsevier: 1993. p. 81\u201394."},{"issue":"12","key":"2517_CR39","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/0167-5699(91)90018-O","volume":"12","author":"O R\u00f6tzschke","year":"1991","unstructured":"R\u00f6tzschke O, Falk K. Naturally-occurring peptide antigens derived from the mhc class-i-restricted processing pathway. Immunol Today. 1991; 12(12):447\u201355.","journal-title":"Immunol Today"},{"issue":"7","key":"2517_CR40","first-page":"0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach S, Binder A, Montavon G, Klauschen F, M\u00fcller K-R, Samek W. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS ONE. 2015; 10(7):0130140.","journal-title":"PloS ONE"},{"key":"2517_CR41","doi-asserted-by":"crossref","unstructured":"Bhattacharya R, Sivakumar A, Tokheim C, Guthrie VB, Anagnostou V, Velculescu VE, Karchin R. Evaluation of machine learning methods to predict peptide binding to mhc class i proteins. bioRxiv. 2017;:154757. \n                    https:\/\/www.biorxiv.org\/content\/early\/2017\/07\/27\/154757.abstract\n                    \n                  .","DOI":"10.1101\/154757"},{"key":"2517_CR42","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neucom.2016.09.010","volume":"219","author":"S Yu","year":"2017","unstructured":"Yu S, Jia S, Xu C. Convolutional neural networks for hyperspectral image classification. Neurocomputing. 2017; 219:88\u201398.","journal-title":"Neurocomputing"},{"key":"2517_CR43","volume-title":"Data Mining: Practical Machine Learning Tools and Techniques","author":"IH Witten","year":"2016","unstructured":"Witten IH, Frank E, Hall MA, Pal CJ. Data Mining: Practical Machine Learning Tools and Techniques. San Francisco: Morgan Kaufmann; 2016."},{"issue":"1","key":"2517_CR44","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","volume":"134","author":"P-T De Boer","year":"2005","unstructured":"De Boer P-T, Kroese DP, Mannor S, Rubinstein RY. A tutorial on the cross-entropy method. Ann Oper Res. 2005; 134(1):19\u201367.","journal-title":"Ann Oper Res"},{"key":"2517_CR45","volume-title":"Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference On","author":"B Zhou","year":"2016","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A. Learning deep features for discriminative localization. In: Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference On. New York: IEEE: 2016. p. 2921\u20139."},{"key":"2517_CR46","volume-title":"MHC Ligands and Peptide Motifs","author":"H-G Rammensee","year":"2013","unstructured":"Rammensee H-G, Bachmann J, Stevanovic S. MHC Ligands and Peptide Motifs. Berlin\/Heidelberg: Springer; 2013."},{"issue":"3","key":"2517_CR47","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1109\/TPAMI.2013.140","volume":"36","author":"CH Lampert","year":"2014","unstructured":"Lampert CH, Nickisch H, Harmeling S. Attribute-based classification for zero-shot visual object categorization. IEEE Trans Pattern Anal Mach Intell. 2014; 36(3):453\u201365.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"W1","key":"2517_CR48","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1093\/nar\/gks438","volume":"40","author":"Y Kim","year":"2012","unstructured":"Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, Lundegaard C, Sette A, Lund O, Bourne PE, et al.Immune epitope database analysis resource. Nucleic Acids Res. 2012; 40(W1):525\u201330.","journal-title":"Nucleic Acids Res"},{"key":"2517_CR49","unstructured":"Chollet F, et al.Keras. GitHub. 2015."},{"key":"2517_CR50","volume-title":"Advances in Neural Information Processing Systems 27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, editors. Advances in Neural Information Processing Systems 27. USA: Curran Associates, Inc.: 2014. p. 2672\u201380."},{"key":"2517_CR51","unstructured":"Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B. Adversarial autoencoders. arXiv preprint arXiv:1511.05644. 2015. \n                    https:\/\/arxiv.org\/abs\/1511.05644\n                    \n                  ."},{"issue":"1","key":"2517_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00251-008-0341-z","volume":"61","author":"I Hoof","year":"2009","unstructured":"Hoof I, Peters B, Sidney J, Pedersen LE, Sette A, Lund O, Buus S, Nielsen M. Netmhcpan, a method for mhc class i binding prediction beyond humans. Immunogenetics. 2009; 61(1):1.","journal-title":"Immunogenetics"},{"key":"2517_CR53","doi-asserted-by":"publisher","first-page":"32115","DOI":"10.1038\/srep32115","volume":"6","author":"H Luo","year":"2016","unstructured":"Luo H, Ye H, Ng HW, Sakkiah S, Mendrick DL, Hong H. snebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides. Sci Rep. 2016; 6:32115.","journal-title":"Sci Rep"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-018-2517-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-018-2517-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-018-2517-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T19:04:00Z","timestamp":1577732640000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-018-2517-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12]]},"references-count":53,"journal-issue":{"issue":"S19","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["2517"],"URL":"https:\/\/doi.org\/10.1186\/s12859-018-2517-3","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12]]},"assertion":[{"value":"31 December 2018","order":1,"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"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Publisher\u2019s Note"}}],"article-number":"516"}}