{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:50:06Z","timestamp":1761598206275,"version":"3.37.3"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2020,5,14]],"date-time":"2020-05-14T00:00:00Z","timestamp":1589414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,14]],"date-time":"2020-05-14T00:00:00Z","timestamp":1589414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61365001","61463052","61966037"],"award-info":[{"award-number":["61365001","61463052","61966037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007846","name":"Yunnan Provincial Department of Education","doi-asserted-by":"publisher","award":["2019J0006","2019Y0003"],"award-info":[{"award-number":["2019J0006","2019Y0003"]}],"id":[{"id":"10.13039\/501100007846","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Yunnan Province University Key Laboratory Construction Plan Funding, China"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s00521-020-04879-7","type":"journal-article","created":{"date-parts":[[2020,5,14]],"date-time":"2020-05-14T13:02:52Z","timestamp":1589461372000},"page":"15557-15571","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Attentive gated neural networks for identifying chromatin accessibility"],"prefix":"10.1007","volume":"32","author":[{"given":"Yanbu","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0139-9415","authenticated-orcid":false,"given":"Dongming","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihua","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rencan","family":"Nie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruichao","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengli","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,14]]},"reference":[{"issue":"8","key":"4879_CR1","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1093\/bioinformatics\/btw785","volume":"33","author":"Y Piao","year":"2016","unstructured":"Piao Y, Lee SK, Lee E-J, Robertson KD, Shi H, Ryu KH, Choi J-H (2016) CAME: identification of chromatin accessibility from nucleosome occupancy and methylome sequencing. Bioinformatics 33(8):1139\u20131146","journal-title":"Bioinformatics"},{"issue":"5","key":"4879_CR2","doi-asserted-by":"publisher","first-page":"732","DOI":"10.1093\/bioinformatics\/btx679","volume":"34","author":"Q Liu","year":"2017","unstructured":"Liu Q, Xia F, Yin Q, Jiang R (2017) Chromatin accessibility prediction via a hybrid deep convolutional neural network. Bioinformatics 34(5):732\u2013738","journal-title":"Bioinformatics"},{"issue":"14","key":"4879_CR3","doi-asserted-by":"publisher","first-page":"i92","DOI":"10.1093\/bioinformatics\/btx234","volume":"33","author":"X Min","year":"2017","unstructured":"Min X, Zeng W, Chen N, Chen T, Jiang R (2017) Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding. Bioinformatics 33(14):i92\u2013i101","journal-title":"Bioinformatics"},{"issue":"2","key":"4879_CR4","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1056\/NEJMra0905980","volume":"363","author":"TA Manolio","year":"2010","unstructured":"Manolio TA (2010) Genomewide association studies and assessment of the risk of disease. N Engl J Med 363(2):166\u2013176","journal-title":"N Engl J Med"},{"issue":"2","key":"4879_CR5","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1534\/genetics.110.120907","volume":"187","author":"BE Stranger","year":"2011","unstructured":"Stranger BE, Stahl EA, Raj T (2011) Progress and promise of genome-wide association studies for human complex trait genetics. Genetics 187(2):367\u2013383","journal-title":"Genetics"},{"issue":"1","key":"4879_CR6","doi-asserted-by":"publisher","first-page":"S00928674183017","DOI":"10.1016\/j.cell.2018.02.028","volume":"173","author":"L Gao","year":"2018","unstructured":"Gao L, Wu K, Liu Z, Yao X, Yuan S, Tao W, Yi L, Yu G, Hou Z, Fan D (2018) Chromatin accessibility landscape in human early embryos and its association with evolution. Cell 173(1):S0092867418301727","journal-title":"Cell"},{"issue":"10","key":"4879_CR7","doi-asserted-by":"publisher","first-page":"e60","DOI":"10.1093\/nar\/gkz167","volume":"47","author":"W Li","year":"2019","unstructured":"Li W, Wong WH, Jiang R (2019) DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning. Nucleic Acids Res 47(10):e60","journal-title":"Nucleic Acids Res"},{"issue":"5830","key":"4879_CR8","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.1126\/science.1141319","volume":"316","author":"DS Johnson","year":"2007","unstructured":"Johnson DS, Mortazavi A, Myers RM, Wold BJ (2007) Genome-wide mapping of in vivo protein-DNA interactions. Science 316(5830):1497\u20131502","journal-title":"Science"},{"issue":"1","key":"4879_CR9","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1101\/gr.4074106","volume":"16","author":"GE Crawford","year":"2005","unstructured":"Crawford GE, Holt I, Whittle J, Webb BD, Tai D, Davis S, Margulies EH, Chen Y, Bernat JA, Ginsburg D (2005) Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome Res 16(1):123\u2013131","journal-title":"Genome Res"},{"issue":"6","key":"4879_CR10","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1101\/gr.5533506","volume":"17","author":"PG Giresi","year":"2007","unstructured":"Giresi PG, Kim J, Mcdaniell RM, Iyer VR, Lieb JD (2007) FAIRE (formaldehyde-assisted isolation of regulatory elements) isolates active regulatory elements from human chromatin. Genome Res 17(6):877\u2013885","journal-title":"Genome Res"},{"issue":"1","key":"4879_CR11","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/nmeth.2713","volume":"11","author":"J Vierstra","year":"2014","unstructured":"Vierstra J, Wang H, John S, Sandstrom R, Stamatoyannopoulos JA (2014) Coupling transcription factor occupancy to nucleosome architecture with DNase-FLASH. Nat Methods 11(1):66\u201372","journal-title":"Nat Methods"},{"key":"4879_CR12","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.neucom.2019.10.091","volume":"379","author":"Y Guo","year":"2020","unstructured":"Guo Y, Zhou D, Nie R, Ruan X, Li W (2020) DeepANF: a deep attentive neural framework with distributed representation for chromatin accessibility prediction. Neurocomputing 379:305\u2013318","journal-title":"Neurocomputing"},{"issue":"1","key":"4879_CR13","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1186\/s12859-019-2940-0","volume":"20","author":"Y Guo","year":"2019","unstructured":"Guo Y, Li W, Wang B, Liu H, Zhou D (2019) DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction. BMC Bioinform 20(1):341","journal-title":"BMC Bioinform"},{"issue":"5","key":"4879_CR14","doi-asserted-by":"publisher","first-page":"1850021","DOI":"10.1142\/S021972001850021X","volume":"16","author":"Y Guo","year":"2018","unstructured":"Guo Y, Wang B, Li W, Yang B (2018) Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks. J Bioinform Comput Biol 16(5):1850021","journal-title":"J Bioinform Comput Biol"},{"key":"4879_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2019.2901789","author":"F Jing","year":"2019","unstructured":"Jing F, Zhang S, Cao Z, Zhang S (2019) An integrative framework for combining sequence and epigenomic data to predict transcription factor binding sites using deep learning. IEEE ACM Trans Comput Biol Bioinf. https:\/\/doi.org\/10.1109\/TCBB.2019.2901789","journal-title":"IEEE ACM Trans Comput Biol Bioinf"},{"key":"4879_CR16","doi-asserted-by":"crossref","unstructured":"Singh R, Lanchantin J, Sekhon A, Qi Y (2017) Attend and predict: understanding gene regulation by selective attention on chromatin. In: Advances in neural information processing systems, 2017, pp 6785\u20136795","DOI":"10.1101\/329334"},{"issue":"12","key":"4879_CR17","doi-asserted-by":"publisher","first-page":"2167","DOI":"10.1101\/gr.121905.111","volume":"21","author":"D Lee","year":"2011","unstructured":"Lee D, Karchin R, Beer MA (2011) Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res 21(12):2167\u20132180","journal-title":"Genome Res"},{"issue":"7","key":"4879_CR18","doi-asserted-by":"publisher","first-page":"e1003711","DOI":"10.1371\/journal.pcbi.1003711","volume":"10","author":"M Ghandi","year":"2014","unstructured":"Ghandi M, Lee D, Mohammadnoori M, Beer M (2014) Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput Biol 10(7):e1003711","journal-title":"PLoS Comput Biol"},{"issue":"7553","key":"4879_CR19","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436","journal-title":"Nature"},{"key":"4879_CR20","doi-asserted-by":"publisher","first-page":"104891","DOI":"10.1016\/j.knosys.2019.104891","volume":"184","author":"A G\u00f3mez-R\u00edos","year":"2019","unstructured":"G\u00f3mez-R\u00edos A, Tabik S, Luengo J, Shihavuddin A, Herrera F (2019) Coral species identification with texture or structure images using a two-level classifier based on convolutional neural networks. Knowl Based Syst 184:104891","journal-title":"Knowl Based Syst"},{"key":"4879_CR21","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2019, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"4879_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04301-x","author":"M-Y Chen","year":"2019","unstructured":"Chen M-Y, Chiang H-S, Sangaiah AK, Hsieh T-C (2019) Recurrent neural network with attention mechanism for language model. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04301-x","journal-title":"Neural Comput Appl"},{"key":"4879_CR23","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2018.09.069","volume":"325","author":"V-K Tran","year":"2019","unstructured":"Tran V-K, Nguyen L-M (2019) Gating mechanism based natural language generation for spoken dialogue systems. Neurocomputing 325:48\u201358","journal-title":"Neurocomputing"},{"key":"4879_CR24","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.knosys.2017.06.003","volume":"132","author":"UR Acharya","year":"2017","unstructured":"Acharya UR, Fujita H, Lih OS, Adam M, Tan JH, Chua CK (2017) Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowl Based Syst 132:62\u201371","journal-title":"Knowl Based Syst"},{"key":"4879_CR25","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.knosys.2017.01.023","volume":"121","author":"M Pei","year":"2017","unstructured":"Pei M, Wu X, Guo Y, Fujita H (2017) Small bowel motility assessment based on fully convolutional networks and long short-term memory. Knowl Based Syst 121:163\u2013172","journal-title":"Knowl Based Syst"},{"issue":"10","key":"4879_CR26","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1038\/nmeth.3547","volume":"12","author":"J Zhou","year":"2015","unstructured":"Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 12(10):931\u2013934","journal-title":"Nat Methods"},{"issue":"7","key":"4879_CR27","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, Rin JL (2016) Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res 26(7):990","journal-title":"Genome Res"},{"issue":"10","key":"4879_CR28","doi-asserted-by":"publisher","first-page":"1705","DOI":"10.1093\/bioinformatics\/bty003","volume":"34","author":"J Zhang","year":"2018","unstructured":"Zhang J, Peng W, Wang L (2018) LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks. Bioinformatics 34(10):1705\u20131712","journal-title":"Bioinformatics"},{"key":"4879_CR29","doi-asserted-by":"publisher","first-page":"6893","DOI":"10.1007\/s00521-018-3523-0","volume":"31","author":"CO Sakar","year":"2018","unstructured":"Sakar CO, Polat SO, Katircioglu M, Kastro Y (2018) Real-time prediction of online shoppers\u2019 purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Comput Appl 31:6893\u20136908","journal-title":"Neural Comput Appl"},{"issue":"2","key":"4879_CR30","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1109\/TII.2016.2601521","volume":"13","author":"H Zhang","year":"2016","unstructured":"Zhang H, Li J, Ji Y, Yue H (2016) Understanding subtitles by character-level sequence-to-sequence learning. IEEE Trans Ind Inf 13(2):616\u2013624","journal-title":"IEEE Trans Ind Inf"},{"issue":"15","key":"4879_CR31","doi-asserted-by":"publisher","first-page":"2642","DOI":"10.1093\/bioinformatics\/bty178","volume":"34","author":"KK Yang","year":"2018","unstructured":"Yang KK, Wu Z, Bedbrook CN, Arnold FH (2018) Learned protein embeddings for machine learning. Bioinformatics 34(15):2642\u20132648","journal-title":"Bioinformatics"},{"key":"4879_CR32","unstructured":"Ng P (2017) dna2vec: consistent vector representations of variable-length k-mers. arXiv preprint arXiv:170106279"},{"key":"4879_CR33","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.neucom.2018.04.036","volume":"305","author":"X Pan","year":"2018","unstructured":"Pan X, Shen H-B (2018) Learning distributed representations of RNA sequences and its application for predicting RNA-protein binding sites with a convolutional neural network. Neurocomputing 305:51\u201358","journal-title":"Neurocomputing"},{"key":"4879_CR34","first-page":"5998","volume":"2017","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141 (2017) Polosukhin I attention is all you need. Adv Neural Inf Process Syst 2017:5998\u20136008","journal-title":"Adv Neural Inf Process Syst"},{"key":"4879_CR35","unstructured":"Dauphin YN, Fan A, Auli M, Grangier D (2016) Language modeling with gated convolutional networks. In: International conference on international conference on machine learning, pp 933\u2013941"},{"key":"4879_CR36","doi-asserted-by":"crossref","unstructured":"Xue W, Li T (2018) Aspect Based sentiment analysis with gated convolutional networks. In: Proceedings of the 56th annual meeting of the association for computational linguistics, 2018, pp 2514\u20132523","DOI":"10.18653\/v1\/P18-1234"},{"key":"4879_CR37","doi-asserted-by":"crossref","unstructured":"Cho K, Van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing, pp 1724\u20131734","DOI":"10.3115\/v1\/D14-1179"},{"issue":"5696","key":"4879_CR38","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1126\/science.1105136","volume":"306","author":"Consortium EP","year":"2004","unstructured":"Consortium EP (2004) The ENCODE (ENCyclopedia Of DNA Elements) project. Science 306(5696):636\u2013640","journal-title":"Science"},{"key":"4879_CR39","unstructured":"Den Oord AV, Kalchbrenner N, Vinyals O, Espeholt L, Graves A, Kavukcuoglu K (2016) Conditional image generation with PixelCNN decoders. In: Advances in neural information processing systems, pp 4797\u20134805"},{"key":"4879_CR40","first-page":"1","volume":"99","author":"H Zhang","year":"2018","unstructured":"Zhang H, Wang S, Xu X, Chow TW, Wu QJ (2018) Tree2Vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst 99:1\u201315","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4879_CR41","unstructured":"Klambauer G, Unterthiner T, Mayr A, Hochreiter S (2017) Self-normalizing neural networks. In: Advances in neural information processing systems, pp 971\u2013980"},{"key":"4879_CR42","doi-asserted-by":"crossref","unstructured":"Han J, Moraga C (1995) The influence of the sigmoid function parameters on the speed of backpropagation learning. In: International workshop on artificial neural networks, 1995. Springer, pp 195\u2013201","DOI":"10.1007\/3-540-59497-3_175"},{"issue":"10","key":"4879_CR43","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1038\/s41588-018-0207-8","volume":"50","author":"JM Kirk","year":"2018","unstructured":"Kirk JM, Kim SO, Inoue K, Smola MJ, Lee DM, Schertzer MD, Wooten JS, Baker AR, Sprague D, Collins DW (2018) Functional classification of long non-coding RNAs by k-mer content. Nat Genet 50(10):1474\u20131482","journal-title":"Nat Genet"},{"key":"4879_CR44","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing, 2014, pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"4879_CR45","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: International conference on international conference on machine learning, 2010, pp 807\u2013814"},{"key":"4879_CR46","unstructured":"Boureau Y-L, Ponce J, LeCun Y (2010) A theoretical analysis of feature pooling in visual recognition. In: Proceedings of the 27th international conference on machine learning, 2010, pp 111\u2013118"},{"key":"4879_CR47","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:14123555"},{"key":"4879_CR48","doi-asserted-by":"crossref","unstructured":"He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: IEEE conference on computer vision and pattern recognition, 2015, pp 5353\u20135360","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"4879_CR49","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2019.2931735","author":"Y Yuan","year":"2019","unstructured":"Yuan Y, Ning H, Lu X (2019) Bio-inspired representation learning for visual attention prediction. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/tcyb.2019.2931735","journal-title":"IEEE Trans Cybern"},{"key":"4879_CR50","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/978-3-642-35289-8_3","volume-title":"Neural networks: tricks of the trade","author":"YA LeCun","year":"2012","unstructured":"LeCun YA, Bottou L, Orr GB, M\u00fcller K-R (2012) Efficient backprop. In: Goos G, Hartmanis J (eds) Neural networks: tricks of the trade. Springer, Berlin, pp 9\u201348"},{"key":"4879_CR51","unstructured":"Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: International conference on learning representations"},{"issue":"1","key":"4879_CR52","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"issue":"3","key":"4879_CR53","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1038\/ng.759","volume":"43","author":"S John","year":"2011","unstructured":"John S, Sabo PJ, Thurman RE, Sung M-H, Biddie SC, Johnson TA, Hager GL, Stamatoyannopoulos JA (2011) Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nat Genet 43(3):264","journal-title":"Nat Genet"},{"key":"4879_CR54","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, 2010, pp 249\u2013256"},{"key":"4879_CR55","first-page":"887","volume":"2018","author":"Z Yin","year":"2018","unstructured":"Yin Z, Shen Y (2018) On the dimensionality of word embedding. Adv Neural Inf Process Syst 2018:887\u2013898","journal-title":"Adv Neural Inf Process Syst"},{"issue":"13","key":"4879_CR56","doi-asserted-by":"publisher","first-page":"i457","DOI":"10.1093\/bioinformatics\/bty294","volume":"34","author":"M Zitnik","year":"2018","unstructured":"Zitnik M, Agrawal M, Leskovec J (2018) Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13):i457\u2013i466","journal-title":"Bioinformatics"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04879-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-04879-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04879-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T02:47:17Z","timestamp":1696128437000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-04879-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,14]]},"references-count":56,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["4879"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-04879-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2020,5,14]]},"assertion":[{"value":"20 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}