{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:27:16Z","timestamp":1740122836396,"version":"3.37.3"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T00:00:00Z","timestamp":1599696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T00:00:00Z","timestamp":1599696000000},"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":["61972333"],"award-info":[{"award-number":["61972333"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802328"],"award-info":[{"award-number":["61802328"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771415"],"award-info":[{"award-number":["61771415"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2019JJ50606"],"award-info":[{"award-number":["2019JJ50606"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014472","name":"Scientific Research Foundation of Hunan Provincial Education Department","doi-asserted-by":"crossref","award":["19B561"],"award-info":[{"award-number":["19B561"]}],"id":[{"id":"10.13039\/100014472","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Baidu Pinecone Program"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1007\/s11042-020-09598-8","type":"journal-article","created":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T19:08:41Z","timestamp":1599764921000},"page":"17239-17255","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DeepRibSt: a multi-feature convolutional neural network for predicting ribosome stalling"],"prefix":"10.1007","volume":"80","author":[{"given":"Yuan","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Sai","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xizhi","family":"He","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Lu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7764-3616","authenticated-orcid":false,"given":"Xieping","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,10]]},"reference":[{"issue":"8","key":"9598_CR1","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1038\/nbt.3300","volume":"33","author":"B Alipanahi","year":"2015","unstructured":"Alipanahi B, Delong A, Weirauch MT, Frey BJ (2015) Predicting the sequence specificities of DNA and RNA-binding proteins by deep learning. Nat Biotech 33(8):831\u2013838","journal-title":"Nat Biotech"},{"issue":"10","key":"9598_CR2","first-page":"2825","volume":"12","author":"S Ashish","year":"2012","unstructured":"Ashish S, jain Ritesh (2012) Scikit-learn: Machine Learning in Python. J Mach Learn Res 12(10):2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"9598_CR3","doi-asserted-by":"crossref","unstructured":"Ashkenazy H, Erez E, Martz E, Pupko T, Tal NB (2010) Consurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids Nucleic Acids Res W529\u2013W533","DOI":"10.1093\/nar\/gkq399"},{"issue":"6222","key":"9598_CR4","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1126\/science.1260793","volume":"347","author":"A Battle","year":"2015","unstructured":"Battle A, Khan Z, Wang SH, Mitrano A, Ford MJ, Pritchard JK, Gilad Y (2015) Impact of regulatory variation from RNA to protein. Science 347(6222):664\u2013667","journal-title":"Science"},{"issue":"6078","key":"9598_CR5","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1126\/science.1215704","volume":"336","author":"AA Bazzini","year":"2012","unstructured":"Bazzini AA, Lee MT, Giraldez AJ (2012) Ribosome Profiling Shows That miR-430 Reduces Translation Before Causing mRNA Decay in Zebrafish. Science 336(6078):233\u2013237","journal-title":"Science"},{"issue":"2","key":"9598_CR6","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.celrep.2014.09.011","volume":"9","author":"L Bischoff","year":"2014","unstructured":"Bischoff L, Berninghausen O, Beckmann R (2014) Molecular basis for the ribosome functioning as an L-Tryptophan sensor. Cell Reports 9(2):469\u2013475","journal-title":"Cell Reports"},{"issue":"6","key":"9598_CR7","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1016\/j.ccr.2004.05.027","volume":"5","author":"MA Bjornsti","year":"2004","unstructured":"Bjornsti MA, Houghton PJ (2004) Lost in translation: Dysregulation of cap-dependent translation and cancer. Cancer Cell 5(6):519\u2013523","journal-title":"Cancer Cell"},{"issue":"5","key":"9598_CR8","doi-asserted-by":"publisher","first-page":"3227","DOI":"10.1007\/s12035-015-9229-8","volume":"53","author":"A Borreca","year":"2016","unstructured":"Borreca A, Gironi K, Amadoro G, Ammassari-Teule M (2016) Opposite dysregulation of Fragile-X mental retardation protein and heteronuclear ribonucleoprotein c protein associates with enhanced APP translation in alzheimer disease. Molecular Neurobiology 53(5):3227\u20133234","journal-title":"Molecular Neurobiology"},{"key":"9598_CR9","unstructured":"Bottou L, Bengio Y, Cun YL (1997) Global training of document processing systems using graph transformer networks IEEE conference on computer vision and pattern recognition 489\u2013494"},{"issue":"11","key":"9598_CR10","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1038\/nrm4069","volume":"16","author":"GA Brar","year":"2015","unstructured":"Brar GA, Weissman JS (2015) Ribosome profiling reveals the what, when, where and how of proteinsynthesis. Nat Rev Mol Cell Biol 16(11):651\u2013664","journal-title":"Nat Rev Mol Cell Biol"},{"issue":"6068","key":"9598_CR11","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1126\/science.1215110","volume":"335","author":"GA Brar","year":"2012","unstructured":"Brar GA, Yassour M, Friedman N, Regev A, Ingolia NT, Weissman JS (2012) High-Resolution View of the yeast meiotic program revealed by ribosome profiling. Science 335(6068):552\u2013557","journal-title":"Science"},{"issue":"130","key":"9598_CR12","first-page":"20","volume":"130","author":"BB Brodie","year":"1960","unstructured":"Brodie BB, Kurz H, Schanker LS (1960) The importance of dissociation constant and lipid-solubility in influencing the passage of drugs into the cerebrospinal fluid. J Pharmacol Exp Ther 130(130):20\u201325","journal-title":"J Pharmacol Exp Ther"},{"key":"9598_CR13","doi-asserted-by":"publisher","unstructured":"Chakraborty R, Hasija Y (2019) Predicting microRNA sequence using CNN and LSTM stacked in Seq2Seq architecture. IEEE\/ACM Transactions on Computational Biology and Bioinformatics, https:\/\/doi.org\/10.1109\/TCBB.2019.2936186. [Epub ahead of print]","DOI":"10.1109\/TCBB.2019.2936186"},{"key":"9598_CR14","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1146\/annurev-biophys-060414-034333","volume":"44","author":"JL Chaney","year":"2015","unstructured":"Chaney JL, Clark PL (2015) Roles for synonymous codon usage in protein biogenesis. Annu Rev Biophys 44:143\u2013166","journal-title":"Annu Rev Biophys"},{"key":"9598_CR15","doi-asserted-by":"crossref","unstructured":"Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: proceedings of the 25th international conference on machine learning, pp 160\u2013167","DOI":"10.1145\/1390156.1390177"},{"issue":"5","key":"9598_CR16","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/j.jvoice.2018.02.003","volume":"33","author":"SH Fang","year":"2019","unstructured":"Fang SH, Tsao Y, Hsiao MJ, Chen JY, Lai YH, Lin FC, Wang CT (2019) Detection of pathological voice using cepstrum vectors: a deep learning approach. J Voice 33(5):634\u2013641","journal-title":"J Voice"},{"key":"9598_CR17","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep Residual learning for Image Recognition, https:\/\/doi.org\/10.1109\/CVPR.2016.90 IEEE conference on computer vision pattern recognition 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9598_CR18","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.neucom.2019.07.079","volume":"365","author":"K Hu","year":"2019","unstructured":"Hu K, Shen BW, Zhang Y, Cao CH, Xiao F, Gao XP (2019) Automatic segmentation of retinal layer boundaries in OCT images using multiscale convolutional neural network and graph search. Neurocomputing 365:302\u2013313","journal-title":"Neurocomputing"},{"key":"9598_CR19","unstructured":"Huang Z, Xu W, Yu K (2015) Bidirectional LSTM-CRF models for sequence tagging. arXiv:1508.01991"},{"issue":"1","key":"9598_CR20","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1093\/nar\/30.1.38","volume":"30","author":"T Hubbard","year":"2002","unstructured":"Hubbard T (2002) The Ensembl genome database project. Nucleic Acids Res 30(1):38\u201341","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"9598_CR21","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.cell.2016.02.066","volume":"165","author":"NT Ingolia","year":"2016","unstructured":"Ingolia NT (2016) Ribosome footprint profiling of translation throughout the genome. Cell 165(1):22\u201333","journal-title":"Cell"},{"issue":"5924","key":"9598_CR22","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1126\/science.1168978","volume":"324","author":"NT Ingolia","year":"2009","unstructured":"Ingolia NT, Ghaemmaghami S, Newman JRS, Weissman JS (2009) Genome-Wide Analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324(5924):218\u2013223","journal-title":"Science"},{"issue":"4","key":"9598_CR23","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1016\/j.cell.2011.10.002","volume":"147","author":"NT Ingolia","year":"2011","unstructured":"Ingolia NT, Lareau LF, Weissman JS (2011) Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell 147(4):789\u2013802","journal-title":"Cell"},{"issue":"3","key":"9598_CR24","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1016\/j.bbagen.2013.10.035","volume":"1840","author":"P Johnsson","year":"2014","unstructured":"Johnsson P, Lipovich L, Grand\u00e9r D, Morris KV (2014) Evolutionary conservation of long non-coding RNAs; sequence, structure, function. Biochimica Et Biophysica Acta 1840(3):1063\u201371","journal-title":"Biochimica Et Biophysica Acta"},{"key":"9598_CR25","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems Curran Associates Inc, pp 1097\u20131105"},{"issue":"11","key":"9598_CR26","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"issue":"10","key":"9598_CR27","first-page":"e1000963","volume":"6","author":"D Lucent","year":"2010","unstructured":"Lucent D, Snow CD, Aitken CE, Pande VS (2010) Non-Bulk-Like Solvent behavior in the ribosome exit tunnel. PLOS 6(10):e1000963","journal-title":"PLOS"},{"issue":"11","key":"9598_CR28","doi-asserted-by":"publisher","first-page":"2219","DOI":"10.1101\/gr.133249.111","volume":"22","author":"AM Michel","year":"2012","unstructured":"Michel AM, Choudhury KR, Firth AE, Ingolia NT (2012) Observation of dually decoded regions of the human genome using ribosome profiling data. Genome Res 22(11):2219\u20132229","journal-title":"Genome Res"},{"issue":"Datebase issue","key":"9598_CR29","doi-asserted-by":"publisher","first-page":"D859","DOI":"10.1093\/nar\/gkt1035","volume":"42","author":"AM Michel","year":"2014","unstructured":"Michel AM, Fox G, M Kiran A, De Bo C, O\u2019Connor PB, Heaphy SM, Mullan JP, Donohue CA, Higgins DG, Baranov PV (2014) GWIPS-Viz: development of a ribo-seq genome browser. Nucleic Acids Res 42(Datebase issue):D859\u201364","journal-title":"Nucleic Acids Res"},{"key":"9598_CR30","unstructured":"Molchanov P, Tyree S, Karras T, Aila T, Kautz J (2017) Pruning convolutional neural networks for resource efficient inference. arXiv:1611.06440v2"},{"key":"9598_CR31","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1186\/s12859-017-1561-8","volume":"18","author":"X Pan","year":"2017","unstructured":"Pan X, Shen H (2017) RNA-Protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. BMC Bioinforma 18:136","journal-title":"BMC Bioinforma"},{"issue":"7","key":"9598_CR32","doi-asserted-by":"publisher","first-page":"e1006185","DOI":"10.1371\/journal.pcbi.1006185","volume":"14","author":"A Pla","year":"2018","unstructured":"Pla A, Zhong X, Rayner S (2018) miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts. PLoS Comput Biol 14(7):e1006185","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"9598_CR33","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1101\/gr.097857.109","volume":"20","author":"KS Pollard","year":"2010","unstructured":"Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A (2010) Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res 20 (1):110\u2013121","journal-title":"Genome Res"},{"issue":"12","key":"9598_CR34","doi-asserted-by":"publisher","first-page":"770","DOI":"10.15252\/msb.20145524","volume":"10","author":"C Pop","year":"2014","unstructured":"Pop C, Rouskin S, Ingolia NT, Han L, Phizicky EM, Weissman JS, Koller D (2014) Causal signals between codon bias, m RNA structure, and the efficiency of translation and elongation. Mol Syst Biol 10(12):770","journal-title":"Mol Syst Biol"},{"issue":"2","key":"9598_CR35","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.molcel.2015.05.035","volume":"59","author":"TE Quan","year":"2015","unstructured":"Quan TE, Claassens NJ, Soll D, van der Oost J (2015) Codon bias as a means to Fine-Tune gene expression. Mol Cell 59(2):149\u201361","journal-title":"Mol Cell"},{"issue":"10","key":"9598_CR36","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1038\/nrg3051","volume":"12","author":"ZE Sauna","year":"2011","unstructured":"Sauna ZE, Kimchi SC (2011) Understanding the contribution of synonymous mutations to human disease. Nat Rev Genet 12(10):683\u2013691","journal-title":"Nat Rev Genet"},{"issue":"8","key":"9598_CR37","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1101\/gr.3715005","volume":"15","author":"A Siepel","year":"2005","unstructured":"Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier LW, Richards S, Weinstock GM, Wilson RK, Gibbs RA, Kent WJ, Miller W, Haussler D (2005) Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res 15 (8):1034\u20131050","journal-title":"Genome Res"},{"issue":"4","key":"9598_CR38","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1016\/j.molcel.2013.09.018","volume":"52","author":"C Stumpf","year":"2013","unstructured":"Stumpf C, Moreno M, Olshen A, Taylor B, Ruggero D (2013) The translational landscape of the mammalian cell cycle. Mol Cell 52(4):574\u2013582","journal-title":"Mol Cell"},{"issue":"50","key":"9598_CR39","doi-asserted-by":"publisher","first-page":"34809","DOI":"10.1074\/jbc.M109.039040","volume":"284","author":"DR Tanner","year":"2009","unstructured":"Tanner DR, Cariello DA, Woolstenhulme CJ, Broadbent MA, Buskirk AR (2009) Genetic identification of nascent peptides that induce ribosome stalling. J Biol Chem 284(50):34809\u201334818","journal-title":"J Biol Chem"},{"issue":"2","key":"9598_CR40","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.jmb.2008.08.012","volume":"383","author":"CJ Tsai","year":"2008","unstructured":"Tsai CJ, Sauna ZE, Kimchi-Sarfaty C, Ambudkar SV, Gottesman MM, Nussinov R (2008) Synonymous mutations and ribosome stalling can lead to altered folding pathways and distinct minima. J Mol Biol 383(2):281\u201391","journal-title":"J Mol Biol"},{"issue":"26","key":"9598_CR41","doi-asserted-by":"publisher","first-page":"10496","DOI":"10.1073\/pnas.1103474108","volume":"108","author":"N V\u00e1zquez-Laslop","year":"2011","unstructured":"V\u00e1zquez-Laslop N, Klepacki D, Mulhearn DC, Ramu H, Krasnykh O, Franzblau S, Mankin AS (2011) Role of antibiotic Iigand in nascent peptide-dependent ribosome stalling. Proceedings of the national academy of sciences of the United States of America 108(26):10496\u2013501","journal-title":"Proceedings of the national academy of sciences of the United States of America"},{"issue":"2","key":"9598_CR42","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.molcel.2008.02.026","volume":"30","author":"N Vazquez-Laslop","year":"2018","unstructured":"Vazquez-Laslop N, Thum C, Mankin AS (2018) Molecular mechanism of drug-dependent ribosome stalling. Mol Cell 30(2):190\u2013202","journal-title":"Mol Cell"},{"issue":"45","key":"9598_CR43","doi-asserted-by":"publisher","first-page":"11418","DOI":"10.1523\/JNEUROSCI.2352-16.2016","volume":"36","author":"ET Wang","year":"2016","unstructured":"Wang ET, Taliaferro JM, Lee JA, Sudhakaran IP, Rossoll W, Gross C, Moss KR, Bassell GJ (2016) Dysregulation of mRNA Localization and Translation in Genetic Disease. JNEUROSCI 36(45):11418\u201311426","journal-title":"JNEUROSCI"},{"issue":"22","key":"9598_CR44","doi-asserted-by":"publisher","first-page":"3781","DOI":"10.1093\/bioinformatics\/bty424","volume":"34","author":"M Wen","year":"2018","unstructured":"Wen M, Cong P, Zhang Z, Lu H, Li T (2018) Deepmirtar: a deep-learning approach for predicting human mi RNA targets. Bioinformatics 34 (22):3781\u20133787","journal-title":"Bioinformatics"},{"issue":"10","key":"9598_CR45","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1038\/nsb1096-842","volume":"3","author":"C Wimley William","year":"1996","unstructured":"Wimley William C, White Stephen H (1996) Experimentally determined hydrophobicity scale for proteins at membrane interfaces. Nat Struct Biol 3(10):842\u2013848","journal-title":"Nat Struct Biol"},{"issue":"D1","key":"9598_CR46","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1093\/nar\/gkv972","volume":"44","author":"SQ Xie","year":"2016","unstructured":"Xie SQ, Nie P, Wang Y, Wang H, Li H, Yang Z, Liu Y, Ren J, Xie Z (2016) RPF Db: a database for genome wide information of translated m RNA generated from ribosome profiling. Nucleic Acids Res 44(D1):254\u20138","journal-title":"Nucleic Acids Res"},{"issue":"12","key":"9598_CR47","doi-asserted-by":"publisher","first-page":"3732","DOI":"10.3390\/ijms19123732","volume":"19","author":"P Xuan","year":"2018","unstructured":"Xuan P, Dong Y, Guo Y, Zhang T, Liu Y (2018) Dual Convolutional Neural Network Based Method for Predicting Disease-Related mi RNA s. International Journal of Molecular Science 19(12):3732","journal-title":"International Journal of Molecular Science"},{"issue":"2","key":"9598_CR48","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1109\/TNNLS.2014.2314123","volume":"26","author":"H Yahong","year":"2015","unstructured":"Yahong H, Yi Y, Yan Y, Ma Z, Sebe N, Zhou X (2015) Semisupervised feature selection via spline regression for video semantic recognition. IEEE Trans Neural Netw Learning Syst 26(2):252\u2013264","journal-title":"IEEE Trans Neural Netw Learning Syst"},{"issue":"4","key":"9598_CR49","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1016\/j.cell.2015.07.041","volume":"162","author":"D Young","year":"2015","unstructured":"Young D, Guydosh N, Zhang F, Hinnebusch A, Green R (2015) Rli1\/ ABCE 1 recycles terminating ribosomes and controls translation reinitiation in 3\u2019 UTR s in vivo. Cell 162(4):872\u2013884","journal-title":"Cell"},{"issue":"12","key":"9598_CR50","doi-asserted-by":"publisher","first-page":"i121","DOI":"10.1093\/bioinformatics\/btw255","volume":"32","author":"H Zeng","year":"2016","unstructured":"Zeng H, Edwards MD, Liu G, Gifford DK (2016) Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics 32(12):i121\u2013i127","journal-title":"Bioinformatics"},{"issue":"4","key":"9598_CR51","doi-asserted-by":"publisher","first-page":"e32","DOI":"10.1093\/nar\/gkv1025","volume":"44","author":"S Zhang","year":"2016","unstructured":"Zhang S, Zhou JT, Hu HL, Gong H, Chen L, Cheng C, Zeng JY (2016) A deep learning framework for modeling structural features of RNA-binding protein targets. Nucleic Acids Res 44(4):e32","journal-title":"Nucleic Acids Res"},{"key":"9598_CR52","doi-asserted-by":"publisher","unstructured":"Zhang S, Hu H, Zhou J, He X, Jiang T, Zeng J (2018) ROSE: A Deep Learning Based Framework for Predicting Ribosome Stalling Cell Systems Available at SSRN: https:\/\/ssrn.com\/abstract=3155721 or https:\/\/doi.org\/10.2139\/ssrn.3155721","DOI":"10.2139\/ssrn.3155721"},{"key":"9598_CR53","first-page":"62","volume":"076101","author":"ZJ Zhou","year":"2019","unstructured":"Zhou ZJ (2019) Abductive learning: Towards bridging machine learning and logical reasoning. Science China Information Sciences 076101:62","journal-title":"Science China Information Sciences"},{"key":"9598_CR54","unstructured":"Zhou J, Lu Q, Xu R, Gui L, Wang H (2016) CNNsite: Prediction of DNA-binding residues in proteins using Convolutional Neural Network with sequence features 2016 IEEE international conference on bioinformatics and biomedicine, pp 78-85"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09598-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09598-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09598-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T00:34:50Z","timestamp":1631234090000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09598-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,10]]},"references-count":54,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,5]]}},"alternative-id":["9598"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09598-8","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2020,9,10]]},"assertion":[{"value":"23 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}