{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T07:28:17Z","timestamp":1769844497014,"version":"3.49.0"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602207"],"award-info":[{"award-number":["61602207"]}],"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":["61572228"],"award-info":[{"award-number":["61572228"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Technological Research Projects in Jilin Province","award":["20190302107GX"],"award-info":[{"award-number":["20190302107GX"]}]},{"name":"Special Research and Development of Industrial Technology of Jilin Province","award":["2019C053-7"],"award-info":[{"award-number":["2019C053-7"]}]},{"name":"Guangdong Premier Key-Discipline Enhancement Scheme","award":["2016GDYSZDXK036"],"award-info":[{"award-number":["2016GDYSZDXK036"]}]},{"name":"Guangdong Key-Project for Applied Fundamental Research","award":["2018KZDXM076"],"award-info":[{"award-number":["2018KZDXM076"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2927253","type":"journal-article","created":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T16:10:09Z","timestamp":1562947809000},"page":"89354-89365","source":"Crossref","is-referenced-by-count":26,"title":["Deep Residual Convolutional Neural Network for Protein-Protein Interaction Extraction"],"prefix":"10.1109","volume":"7","author":[{"given":"Hao","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7162-7826","authenticated-orcid":false,"given":"Renchu","family":"Guan","sequence":"additional","affiliation":[]},{"given":"Fengfeng","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yanchun","family":"Liang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0862-0514","authenticated-orcid":false,"given":"Zhi-Hui","family":"Zhan","sequence":"additional","affiliation":[]},{"given":"Lan","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xiaoyue","family":"Feng","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1504\/IJDMB.2016.076534"},{"key":"ref38","first-page":"1","article-title":"Learning language in logic-genic interaction extraction challenge","author":"n\u00e9dellec","year":"2005","journal-title":"Proc Learning Language in Logic Workshop"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1052"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-2304"},{"key":"ref30","doi-asserted-by":"crossref","DOI":"10.1155\/2016\/8479587","article-title":"A shortest dependency path based convolutional neural network for protein-protein relation extraction","volume":"2016","author":"hua","year":"2016","journal-title":"Biomed Res Int"},{"key":"ref37","first-page":"326","article-title":"Mining MEDLINE: Abstracts, sentences, or phrases?","author":"ding","year":"2002","journal-title":"Proc Pacific Symp Biocomput"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl616"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-8-50"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2004.07.016"},{"key":"ref60","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"9","author":"glorot","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref28","first-page":"1310","article-title":"On the difficulty of training recurrent neural networks","volume":"28","author":"pascanu","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICOSC.2019.8665584"},{"key":"ref29","doi-asserted-by":"crossref","DOI":"10.1155\/2016\/1850404","article-title":"Multichannel convolutional neural network for biological relation extraction","volume":"2016","author":"quan","year":"2016","journal-title":"Biomed Res Int"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-017-1700-2"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1038\/nature04209","article-title":"Towards a proteome-scale map of the human protein–protein interaction network","volume":"437","author":"rual","year":"2005","journal-title":"Nature"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0187379"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2009.04.010"},{"key":"ref21","first-page":"3","article-title":"Exploiting shallow linguistic information for relation extraction from biomedical literature","author":"giuliano","year":"2006","journal-title":"Proc 11th Conf Eur Chapter Assoc Comput Linguistics"},{"key":"ref24","first-page":"2048","article-title":"Show, attend and tell: Neural image caption generation with visual attention","volume":"37","author":"xu","year":"2015","journal-title":"Proceedings of the 32nd Intl Conf on Machine Learning"},{"key":"ref23","article-title":"Neural machine translation by jointly learning to align and translate","author":"bahdanau","year":"2014","journal-title":"arXiv 1409 0473"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.11.020"},{"key":"ref25","first-page":"240","article-title":"Identifying protein-protein interactions in biomedical literature using recurrent neural networks with long short-term memory","volume":"2","author":"hsieh","year":"2017","journal-title":"Proc 8th Int Joint Conf Natural Lang Process"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1283"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.2196\/12957"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W16-2922"},{"key":"ref58","first-page":"2335","article-title":"Relation classification via convolutional deep neural network","author":"zeng","year":"2014","journal-title":"Proc COLING 25th Int Conf Comput Linguistics"},{"key":"ref57","article-title":"Efficient estimation of word representations in vector space","author":"mikolov","year":"2013","journal-title":"arXiv 1301 3781 [cs]"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1177\/0165551516673485"},{"key":"ref55","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref54","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref53","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"37","author":"ioffe","year":"2015","journal-title":"Proceedings of the 32nd Intl Conf on Machine Learning"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-018-0628-4"},{"key":"ref11","first-page":"6.1","article-title":"Syntactic features for protein-protein interaction extraction","author":"saetre","year":"2008","journal-title":"Proc 2nd Int Symp Lang Biol Med (LBM)"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btw486"},{"key":"ref12","first-page":"585","article-title":"Fast kernels for string and tree matching","author":"smola","year":"2003","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref13","first-page":"625","article-title":"Convolution kernels for natural language","author":"collins","year":"2002","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref14","first-page":"318","article-title":"Efficient convolution kernels for dependency and constituent syntactic trees","volume":"4212","author":"moschitti","year":"2006","journal-title":"Machine Learnin"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1527\/tjsai.22.140"},{"key":"ref16","article-title":"Extracting relations from biomedical texts using syntactic information","author":"palaga","year":"2009"},{"key":"ref17","first-page":"228","article-title":"Semi-supervised classification for extracting protein interaction sentences using dependency parsing","author":"erkan","year":"2007","journal-title":"Proc Joint Conf Empirical Methods Natural Lang Process Comput Natural Lang Learn"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-9-S11-S2"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm544"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkr1149"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2005.08.029"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1000837"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-017-1938-8"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-9-S3-S6"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-14-12"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2008-9-s2-s9"},{"key":"ref46","first-page":"1243","article-title":"Convolutional sequence to sequence learning","volume":"70","author":"gehring","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref45","first-page":"933","article-title":"Language modeling with gated convolutional networks","volume":"70","author":"dauphin","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref48","article-title":"Natural language processing (almost) from scratch","author":"collobert","year":"2011","journal-title":"arXiv 1103 0398"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/E17-1104"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0190926"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-019-0736-9"},{"key":"ref44","first-page":"1795","article-title":"Drug-drug interaction relation extraction with deep convolutional neural networks","author":"dewi","year":"2017","journal-title":"Proc IEEE Int Conf Bioinformat Biomed (BIBM)"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.3390\/e21010037"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08756171.pdf?arnumber=8756171","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T15:11:22Z","timestamp":1643296282000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8756171\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":60,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2927253","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}