{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:22:29Z","timestamp":1776442949431,"version":"3.51.2"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T00:00:00Z","timestamp":1574812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T00:00:00Z","timestamp":1574812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31670725 and 91730301"],"award-info":[{"award-number":["31670725 and 91730301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Advanced Innovation Center for Structral Biology","award":["2019"],"award-info":[{"award-number":["2019"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Recurrent neural network(RNN) is a good way to process sequential data, but the capability of RNN to compute long sequence data is inefficient. As a variant of RNN, long short term memory(LSTM) solved the problem in some extent. Here we improved LSTM for big data application in protein-protein interaction interface residue pairs prediction based on the following two reasons. On the one hand, there are some deficiencies in LSTM, such as shallow layers, gradient explosion or vanishing, etc. With a dramatic data increasing, the imbalance between algorithm innovation and big data processing has been more serious and urgent. On the other hand, protein-protein interaction interface residue pairs prediction is an important problem in biology, but the low prediction accuracy compels us to propose new computational methods.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In order to surmount aforementioned problems of LSTM, we adopt the residual architecture and add attention mechanism to LSTM. In detail, we redefine the block, and add a connection from front to back in every two layers and attention mechanism to strengthen the capability of mining information. Then we use it to predict protein-protein interaction interface residue pairs, and acquire a quite good accuracy over 72%. What\u2019s more, we compare our method with random experiments, PPiPP, standard LSTM, and some other machine learning methods. Our method shows better performance than the methods mentioned above.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>We present an attention mechanism enhanced LSTM with residual architecture, and make deeper network without gradient vanishing or explosion to a certain extent. Then we apply it to a significant problem\u2013 protein-protein interaction interface residue pairs prediction and obtain a better accuracy than other methods. Our method provides a new approach for protein-protein interaction computation, which will be helpful for related biomedical researches.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-019-3199-1","type":"journal-article","created":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T15:03:40Z","timestamp":1574867020000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Attention mechanism enhanced LSTM with residual architecture and its application for protein-protein interaction residue pairs prediction"],"prefix":"10.1186","volume":"20","author":[{"given":"Jiale","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinqi","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,11,27]]},"reference":[{"key":"3199_CR1","doi-asserted-by":"crossref","unstructured":"Graves A. Supervised sequence labelling. In: Supervised Sequence Labelling with Recurrent Neural Networks. Springer: 2012. p. 5\u201313.","DOI":"10.1007\/978-3-642-24797-2_2"},{"issue":"8","key":"3199_CR2","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997; 9(8):1735\u201380.","journal-title":"Neural Comput"},{"key":"3199_CR3","doi-asserted-by":"crossref","unstructured":"Cho K, Van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. 2014.","DOI":"10.3115\/v1\/D14-1179"},{"key":"3199_CR4","doi-asserted-by":"crossref","unstructured":"Zhou J, Xu W. End-to-end learning of semantic role labeling using recurrent neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers): 2015. p. 1127\u201337.","DOI":"10.3115\/v1\/P15-1109"},{"key":"3199_CR5","doi-asserted-by":"crossref","unstructured":"Kim J, El-Khamy M, Lee J. Residual lstm: Design of a deep recurrent architecture for distant speech recognition. arXiv preprint arXiv:1701.03360. 2017.","DOI":"10.21437\/Interspeech.2017-477"},{"issue":"7316954","key":"3199_CR6","first-page":"13","volume":"2018","author":"Y Zhao","year":"2018","unstructured":"Zhao Y, Yang R, Chevalier G, Xu X, Zhang Z. Deep residual bidir-lstm for human activity recognition using wearable sensors. Math Problems Engineer. 2018; 2018(7316954):13.","journal-title":"Math Problems Engineer"},{"key":"3199_CR7","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016. p. 770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"key":"3199_CR8","unstructured":"Jozefowicz R, Zaremba W, Sutskever I. An empirical exploration of recurrent network architectures. In: Int Confer Mach Learn.2015. p. 2342\u201350."},{"key":"3199_CR9","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. 2014."},{"key":"3199_CR10","unstructured":"Pradhan S, Longpre S. Exploring the depths of recurrent neural networks with stochastic residual learning. Report. 2016."},{"key":"3199_CR11","unstructured":"Moniz J, Pal C. Convolutional residual memory networks. arXiv preprint arXiv:1606.05262. 2016."},{"issue":"12","key":"3199_CR12","doi-asserted-by":"publisher","first-page":"29104","DOI":"10.1371\/journal.pone.0029104","volume":"6","author":"S Ahmad","year":"2011","unstructured":"Ahmad S, Mizuguchi K. Partner-aware prediction of interacting residues in protein-protein complexes from sequence data. PLoS One. 2011; 6(12):29104.","journal-title":"PLoS One"},{"issue":"7","key":"3199_CR13","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1002\/prot.24479","volume":"82","author":"FuA Afsar Minhas","year":"2014","unstructured":"Afsar Minhas FuA, Geiss BJ, Ben-Hur A. Pairpred: Partner-specific prediction of interacting residues from sequence and structure. Proteins: Struct, Func, Bioinforma. 2014; 82(7):1142\u201355.","journal-title":"Proteins: Struct, Func, Bioinforma"},{"issue":"5","key":"3199_CR14","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1109\/TCBB.2017.2706682","volume":"16","author":"Z Zhao","year":"2017","unstructured":"Zhao Z, Gong X. Protein-protein interaction interface residue pair prediction based on deep learning architecture. IEEE\/ACM Trans Comput Biol Bioinforma. 2017; 16(5):1753\u201359.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma"},{"issue":"1","key":"3199_CR15","doi-asserted-by":"publisher","first-page":"16023","DOI":"10.1038\/s41598-017-16397-z","volume":"7","author":"W Wang","year":"2017","unstructured":"Wang W, Yang Y, Yin J, Gong X. Different protein-protein interface patterns predicted by different machine learning methods. Sci Rep. 2017; 7(1):16023.","journal-title":"Sci Rep"},{"issue":"4","key":"3199_CR16","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.cels.2019.03.006","volume":"8","author":"M AlQuraishi","year":"2019","unstructured":"AlQuraishi M. End-to-end differentiable learning of protein structure. Cell systems. 2019; 8(4):292\u2013301.","journal-title":"Cell systems"},{"key":"3199_CR17","doi-asserted-by":"publisher","first-page":"02030","DOI":"10.7554\/eLife.02030","volume":"3","author":"S Ovchinnikov","year":"2014","unstructured":"Ovchinnikov S, Kamisetty H, Baker D. Robust and accurate prediction of residue\u2013residue interactions across protein interfaces using evolutionary information. Elife. 2014; 3:02030.","journal-title":"Elife"},{"issue":"3","key":"3199_CR18","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1093\/bioinformatics\/btx584","volume":"34","author":"F Nadalin","year":"2017","unstructured":"Nadalin F, Carbone A. Protein\u2013protein interaction specificity is captured by contact preferences and interface composition. Bioinformatics. 2017; 34(3):459\u201368.","journal-title":"Bioinformatics"},{"key":"3199_CR19","doi-asserted-by":"crossref","unstructured":"Ohue M, Matsuzaki Y, Shimoda T, Ishida T, Akiyama Y. Highly precise protein-protein interaction prediction based on consensus between template-based and de novo docking methods. In: BMC Proceedings. BioMed Central: 2013. p. 6.","DOI":"10.1186\/1753-6561-7-S7-S6"},{"issue":"suppl_2","key":"3199_CR20","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1093\/nar\/gkq481","volume":"38","author":"R Singh","year":"2010","unstructured":"Singh R, Park D, Xu J, Hosur R, Berger B. Struct2net: a web service to predict protein\u2013protein interactions using a structure-based approach. Nucleic Acids Res. 2010; 38(suppl_2):508\u201315.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"3199_CR21","doi-asserted-by":"publisher","first-page":"1005324","DOI":"10.1371\/journal.pcbi.1005324","volume":"13","author":"S Wang","year":"2017","unstructured":"Wang S, Sun S, Li Z, Zhang R, Xu J. Accurate de novo prediction of protein contact map by ultra-deep learning model. PLoS Comput Biol. 2017; 13(1):1005324.","journal-title":"PLoS Comput Biol"},{"issue":"19","key":"3199_CR22","doi-asserted-by":"publisher","first-page":"3031","DOI":"10.1016\/j.jmb.2015.07.016","volume":"427","author":"Thom Vreven","year":"2015","unstructured":"Vreven T, Moal IH, Vangone A, Pierce BG, Kastritis PL, Torchala M, Chaleil R, Jim\u00e9nez-Garc\u00eda B, Bates PA, Fernandez-Recio J, et al. Updates to the integrated protein\u2013protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2. J Mole Biol. 2015; 427(19):3031\u201341.","journal-title":"Journal of Molecular Biology"},{"issue":"1","key":"3199_CR23","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1002\/prot.10381","volume":"52","author":"J Janin","year":"2003","unstructured":"Janin J, Henrick K, Moult J, Ten Eyck L, Sternberg MJ, Vajda S, Vakser I, Wodak SJ. Capri: a critical assessment of predicted interactions. Proteins: Structure, Function, and Bioinformatics. 2003; 52(1):2\u20139.","journal-title":"Proteins: Structure, Function, and Bioinformatics"},{"issue":"2","key":"3199_CR24","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.jsb.2005.11.005","volume":"153","author":"TB Fischer","year":"2006","unstructured":"Fischer TB, Holmes JB, Miller IR, Parsons JR, Tung L, Hu JC, Tsai J. Assessing methods for identifying pair-wise atomic contacts across binding interfaces. J Struct Biol. 2006; 153(2):103\u201312.","journal-title":"J Struct Biol"},{"key":"3199_CR25","unstructured":"Hubbard S, Thornton J. Naccess: Department of biochemistry and molecular biology, university college london. 1993. Software available at http:\/\/www.bioinf.manchester.ac.uk\/naccess\/nacdownload.html."},{"issue":"1","key":"3199_CR26","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1146\/annurev.bi.53.070184.003115","volume":"53","author":"D Eisenberg","year":"1984","unstructured":"Eisenberg D. Three-dimensional structure of membrane and surface proteins. Ann Rev Biochem. 1984; 53(1):595\u2013623.","journal-title":"Ann Rev Biochem"},{"issue":"1","key":"3199_CR27","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/0022-2836(82)90515-0","volume":"157","author":"J Kyte","year":"1982","unstructured":"Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J Mole Biol. 1982; 157(1):105\u201332.","journal-title":"J Mole Biol"},{"issue":"2","key":"3199_CR28","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1021\/ct100578z","volume":"7","author":"MH Olsson","year":"2011","unstructured":"Olsson MH, S\u00f8ndergaard CR, Rostkowski M, Jensen JH. Propka3: consistent treatment of internal and surface residues in empirical p k a predictions. J Chem Theory Comput. 2011; 7(2):525\u201337.","journal-title":"J Chem Theory Comput"},{"issue":"2","key":"3199_CR29","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s40484-018-0138-5","volume":"6","author":"Y Yang","year":"2018","unstructured":"Yang Y, Wang W, Lou Y, Yin J, Gong X. Geometric and amino acid type determinants for protein-protein interaction interfaces. Quantitative Biol. 2018; 6(2):163\u201374.","journal-title":"Quantitative Biol"},{"issue":"1","key":"3199_CR30","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014; 15(1):1929\u201358.","journal-title":"J Mach Learn Res"},{"key":"3199_CR31","unstructured":"Zaremba W, Sutskever I, Vinyals O. Recurrent neural network regularization. arXiv preprint arXiv:1409.2329. 2014."},{"key":"3199_CR32","unstructured":"Chorowski JK, Bahdanau D, Serdyuk D, Cho K, Bengio Y. Attention-based models for speech recognition. In: Advances in Neural Information Processing Systems: 2015. p. 577\u201385."},{"key":"3199_CR33","unstructured":"Rockt\u00e4schel T, Grefenstette E, Hermann KM, Ko\u010disky\u0300 T, Blunsom P. Reasoning about entailment with neural attention. arXiv preprint arXiv:1509.06664. 2015."}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3199-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-019-3199-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3199-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T00:16:14Z","timestamp":1606349774000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-019-3199-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,27]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["3199"],"URL":"https:\/\/doi.org\/10.1186\/s12859-019-3199-1","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,27]]},"assertion":[{"value":"30 December 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"No applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"No 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"}}],"article-number":"609"}}