{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:26:42Z","timestamp":1777714002881,"version":"3.51.4"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2018]]},"DOI":"10.1109\/access.2018.2825996","type":"journal-article","created":{"date-parts":[[2018,4,12]],"date-time":"2018-04-12T20:26:04Z","timestamp":1523564764000},"page":"24340-24349","source":"Crossref","is-referenced-by-count":48,"title":["DeepPolyA: A Convolutional Neural Network Approach for Polyadenylation Site Prediction"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3367-3725","authenticated-orcid":false,"given":"Xin","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6059-4267","authenticated-orcid":false,"given":"Zhi","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hakon","family":"Hakonarson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"duchi","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"6097","DOI":"10.1093\/nar\/18.20.6097","article-title":"Sequence logos: A new way to display consensus sequences","volume":"18","author":"schneider","year":"1990","journal-title":"Nucl Acids Res"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/10.9.2997"},{"key":"ref31","author":"simonyan","year":"2014","journal-title":"Very Deep Convolutional Networks for Large-scale Image Recognition"},{"key":"ref30","first-page":"78","article-title":"CNNsite: Prediction of DNA-binding residues in proteins using convolutional neural network with sequence features","author":"zhou","year":"2016","journal-title":"Proc IEEE Int Conf Bioinformat Biomed (BIBM)"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/978-3-642-35289-8_26","article-title":"Practical recommendations for gradient-based training of deep architectures","author":"bengio","year":"2012","journal-title":"Neural Networks Tricks of the Trade"},{"key":"ref36","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc 13th Int Conf Artif Intell Statist"},{"key":"ref35","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref34","author":"hinton","year":"2012","journal-title":"Improving Neural Networks by Preventing Co-adaptation of Feature Detectors"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btw255"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"107e","DOI":"10.1093\/nar\/gkw226","article-title":"DanQ: A hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences","volume":"44","author":"quang","year":"2016","journal-title":"Nucl Acids Res"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1101\/gr.200535.115"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3482"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/wrna.59"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2010.05.015"},{"key":"ref22","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.3300"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.3547"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx264"},{"key":"ref50","author":"simonyan","year":"2013","journal-title":"Deep Inside Convolutional Networks Visualising Image Classification Models and Saliency Maps"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1261\/rna.2107305"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1019732108"},{"key":"ref40","author":"kingma","year":"2014","journal-title":"Adam A method for stochastic optimization"},{"key":"ref12","author":"solovyev","year":"2016","journal-title":"Prediction of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2164-11-646"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl394"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-011-0732-4"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkl870"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btt218"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/30.8.1851"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkn158"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1101\/gr.222331.117"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/nrm.2016.116"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-017-1803-9"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2014.12.001"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1104\/pp.105.060541"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-8-43"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2007-8-2-r24"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/BF00042076"},{"key":"ref46","author":"lanchantin","year":"2016","journal-title":"Deep motif dashboard Visualizing and understanding genomic sequences using deep neural networks"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1101\/gr.849004","article-title":"WebLogo: A sequence logo generator","volume":"14","author":"crooks","year":"2004","journal-title":"Genome Res"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"202w","DOI":"10.1093\/nar\/gkp335","article-title":"Meme suite: tools for motif discovery and searching","volume":"37","author":"bailey","year":"2009","journal-title":"Nucl Acids Res"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"110d","DOI":"10.1093\/nar\/gkv1176","article-title":"JASPAR 2016: A major expansion and update of the open-access database of transcription factor binding profiles","volume":"44","author":"mathelier","year":"2016","journal-title":"Nucl Acids Res"},{"key":"ref42","author":"al-rfou","year":"2016","journal-title":"Theano A Python framework for fast computation of mathematical expressions"},{"key":"ref41","author":"chollet","year":"2015","journal-title":"Keras"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ANZIIS.1994.396988"},{"key":"ref43","author":"abadi","year":"2015","journal-title":"TensorFlow Large-Scale Machine Learning on Heterogeneous Systems"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8274985\/08336859.pdf?arnumber=8336859","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T05:12:11Z","timestamp":1643173931000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8336859\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/access.2018.2825996","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]}}}