{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T03:47:18Z","timestamp":1778644038776,"version":"3.51.4"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"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":"crossref","award":["62076015"],"award-info":[{"award-number":["62076015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Sequenced Protein\u2013Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-023-05594-5","type":"journal-article","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T13:11:49Z","timestamp":1702559509000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["DL-PPI: a method on prediction of sequenced protein\u2013protein interaction based on deep learning"],"prefix":"10.1186","volume":"24","author":[{"given":"Jiahui","family":"Wu","sequence":"first","affiliation":[]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jidong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhihan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jianqiang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"5594_CR1","doi-asserted-by":"publisher","first-page":"bbab036","DOI":"10.1093\/bib\/bbab036","volume":"22","author":"L Hu","year":"2021","unstructured":"Hu L, Wang X, Huang YA, Hu P, You ZH. A survey on computational models for predicting protein\u2013protein interactions. Brief Bioinform. 2021;22:bbab036.","journal-title":"Brief Bioinform"},{"key":"5594_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1759-4499-2-2","volume":"2","author":"K Raman","year":"2010","unstructured":"Raman K. Construction and analysis of protein\u2013protein interaction networks. Autom Exp. 2010;2:1\u201311.","journal-title":"Autom Exp"},{"issue":"001","key":"5594_CR3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/S1672-0229(08)60030-3","volume":"000","author":"P Guda","year":"2009","unstructured":"Guda P, Chittur SV, Guda C. Comparative analysis of protein\u2013protein interactions in cancer-associated genes 25. Genom Proteom Bioinform. 2009;000(001):25\u201336.","journal-title":"Genom Proteom Bioinform"},{"issue":"4","key":"5594_CR4","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1016\/j.cell.2006.03.032","volume":"125","author":"J Lim","year":"2006","unstructured":"Lim J, Tong H, Shaw C, Patel AJ, Szab\u00f3 G, Rual JF, Fisk CJ, Ning L, Smolyar A, Hill DE. A protein\u2013protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell. 2006;125(4):801\u201314.","journal-title":"Cell"},{"issue":"4","key":"5594_CR5","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1002\/pmic.200300636","volume":"4","author":"SH Yook","year":"2004","unstructured":"Yook SH, Oltvai ZN, Barab\u00e1si A. Functional and topological characterization of protein interaction networks. Proteomics. 2004;4(4):928\u201342.","journal-title":"Proteomics"},{"issue":"6230","key":"5594_CR6","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/340245a0","volume":"340","author":"S Fields","year":"1989","unstructured":"Fields S, Song OK. A novel genetic system to detect protein\u2013protein interactions. Nature. 1989;340(6230):245.","journal-title":"Nature"},{"issue":"5644","key":"5594_CR7","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1126\/science.1087361","volume":"302","author":"R Jansen","year":"2003","unstructured":"Jansen R. A Bayesian networks approach for predicting protein\u2013protein interactions from genomic data. Science. 2003;302(5644):449\u201353.","journal-title":"Science"},{"key":"5594_CR8","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1038\/nmeth968","volume":"3","author":"T B\u00fcrckst\u00fcmmer","year":"2006","unstructured":"B\u00fcrckst\u00fcmmer T, Bennett KL, Preradovic A, Sch\u00fctze G, Bauch A. An efficient tandem affinity purification procedure for interaction proteomics in mammalian cells. Nat Methods. 2006;3:1013\u20139.","journal-title":"Nat Methods"},{"key":"5594_CR9","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1093\/bioinformatics\/btab643","volume":"38","author":"Q Yuan","year":"2021","unstructured":"Yuan Q, Chen J, Zhao H, Zhou Y, Yang Y. Structure-aware protein\u2013protein interaction site prediction using deep graph convolutional network. Bioinformatics. 2021;38:125\u201332.","journal-title":"Bioinformatics"},{"issue":"Web Server issu","key":"5594_CR10","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(Web Server issue):508\u201315.","journal-title":"Nucleic Acids Res"},{"issue":"14","key":"5594_CR11","doi-asserted-by":"publisher","first-page":"2397","DOI":"10.1093\/bioinformatics\/btv142","volume":"31","author":"V Miguel","year":"2015","unstructured":"Miguel V, Alfonso V, Tirso P. Structure-PPi: a module for the annotation of cancer-related single-nucleotide variants at protein\u2013protein interfaces. Bioinformatics. 2015;31(14):2397.","journal-title":"Bioinformatics"},{"issue":"12","key":"5594_CR12","first-page":"5031","volume":"9","author":"J Luo","year":"2013","unstructured":"Luo J, Li C. A novel method to predict protein complexes based on gene ontology in PPI networks. J Comput Inf Syst. 2013;9(12):5031\u20139.","journal-title":"J Comput Inf Syst"},{"issue":"1","key":"5594_CR13","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1186\/s12859-022-04850-4","volume":"23","author":"X Wang","year":"2022","unstructured":"Wang X, Zhang Y, Zhou P, Liu X. A supervised protein complex prediction method with network representation learning and gene ontology knowledge. BMC Bioinform. 2022;23(1):300.","journal-title":"BMC Bioinform"},{"key":"5594_CR14","unstructured":"Pitre S. Pipe: a protein\u2013protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs. Ph.D. thesis, Carleton University (Canada); 2010."},{"issue":"10","key":"5594_CR15","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.3390\/ijms17101623","volume":"17","author":"Y Ding","year":"2016","unstructured":"Ding Y, Tang J, Guo F. Identification of protein\u2013protein interactions via a novel matrix-based sequence representation model with amino acid contact information. Int J Mol Sci. 2016;17(10):1623.","journal-title":"Int J Mol Sci"},{"issue":"5","key":"5594_CR16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0125811","volume":"10","author":"ZH You","year":"2015","unstructured":"You ZH, Chan KCC, Hu PW. Predicting protein\u2013protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest. PLoS ONE. 2015;10(5): e0125811.","journal-title":"PLoS ONE"},{"key":"5594_CR17","doi-asserted-by":"publisher","first-page":"2269","DOI":"10.1093\/bioinformatics\/btac104","volume":"38","author":"I Ieremie","year":"2022","unstructured":"Ieremie I, Ewing RM, Niranjan M. Transformergo: predicting protein\u2013protein interactions by modelling the attention between sets of gene ontology terms. Bioinformatics. 2022;38:2269\u201377.","journal-title":"Bioinformatics"},{"key":"5594_CR18","doi-asserted-by":"publisher","first-page":"6481","DOI":"10.1021\/acs.analchem.1c00354","volume":"93","author":"H Cheng","year":"2021","unstructured":"Cheng H, Rao B, Liu L, Cui L, Wei L. PepFormer: end-to-end transformer-based siamese network to predict and enhance peptide detectability based on sequence only. Anal Chem. 2021;93:6481\u201390.","journal-title":"Anal Chem"},{"issue":"20","key":"5594_CR19","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1093\/bioinformatics\/btab321","volume":"37","author":"Q Hou","year":"2021","unstructured":"Hou Q, Bas S, Katharina W, Henriette C, Reza H, Xue F, Sanne A, Jaap H, Anton FK. SeRenDIP-CE: sequence-based interface prediction for conformational epitopes. Bioinformatics. 2021;37(20):20.","journal-title":"Bioinformatics"},{"issue":"1","key":"5594_CR20","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1109\/TCBB.2016.2520923","volume":"14","author":"H Lun","year":"2017","unstructured":"Lun H, Chan K. Extracting coevolutionary features from protein sequences for predicting protein\u2013protein interactions. IEEE\/ACM Trans Comput Biol Bioinform. 2017;14(1):155\u201366.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"11","key":"5594_CR21","doi-asserted-by":"publisher","first-page":"4337","DOI":"10.1073\/pnas.0607879104","volume":"104","author":"J Shen","year":"2007","unstructured":"Shen J, Jian Z, Luo X, Zhu W, Yu K, Chen K, Li Y, Jiang H. Predicting protein\u2013protein interactions based only on sequences information. Proc Natl Acad Sci U S A. 2007;104(11):4337\u201341.","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"10","key":"5594_CR22","first-page":"1","volume":"14","author":"Z-H You","year":"2013","unstructured":"You Z-H, Lei Y-K, Zhu L, Xia J, Wang B. Prediction of protein\u2013protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinform. 2013;14(10):1\u201311.","journal-title":"BMC Bioinform"},{"issue":"1","key":"5594_CR23","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1186\/s12859-017-1700-2","volume":"18","author":"T Sun","year":"2017","unstructured":"Sun T, Zhou B, Lai L, Pei J. Sequence-based prediction of protein protein interaction using a deep-learning algorithm. BMC Bioinform. 2017;18(1):277.","journal-title":"BMC Bioinform"},{"key":"5594_CR24","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1021\/acs.jcim.7b00028","volume":"57","author":"X Du","year":"2017","unstructured":"Du X, Sun S, Hu C, Yao Y, Yan Y, Zhang Y. DeepPPI: boosting prediction of protein\u2013protein interactions with deep neural networks. J Chem Inf Model. 2017;57:1499\u2013510.","journal-title":"J Chem Inf Model"},{"key":"5594_CR25","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/TST.2012.6374366","volume":"17","author":"W Kim","year":"2012","unstructured":"Kim W. Prediction of essential proteins using topological properties in GO-pruned PPI network based on machine learning methods. Tsinghua Sci Technol. 2012;17:645\u201358.","journal-title":"Tsinghua Sci Technol"},{"issue":"1","key":"5594_CR26","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.7717\/peerj.1041","volume":"3","author":"J Zubek","year":"2015","unstructured":"Zubek J, Tatjewski M, Boniecki A, Mnich M, Plewczynski D. Multi-level machine learning prediction of protein\u2013protein interactions in saccharomyces cerevisiae. PeerJ. 2015;3(1):1041.","journal-title":"PeerJ"},{"issue":"4","key":"5594_CR27","doi-asserted-by":"publisher","first-page":"823","DOI":"10.3390\/molecules23040823","volume":"23","author":"T Wang","year":"2018","unstructured":"Wang T, Li L, Huang YA, Zhang H, Ma Y, Zhou X. Prediction of protein\u2013protein interactions from amino acid sequences based on continuous and discrete wavelet transform features. Molecules. 2018;23(4):823.","journal-title":"Molecules"},{"issue":"5","key":"5594_CR28","first-page":"0125811","volume":"10","author":"Y Zhu-Hong","year":"2015","unstructured":"Zhu-Hong Y, Chan KCC, Pengwei H, Franca F. Predicting protein\u2013protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest. PLoS ONE. 2015;10(5):0125811.","journal-title":"PLoS ONE"},{"issue":"S9","key":"5594_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-15-S9-S1","volume":"15","author":"Z-H You","year":"2014","unstructured":"You Z-H, Zhu L, Zheng C-H, Yu H-J, Deng S-P. Prediction of protein\u2013protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set. BMC Bioinform. 2014;15(S9):1\u20139.","journal-title":"BMC Bioinform"},{"key":"5594_CR30","doi-asserted-by":"crossref","unstructured":"You Z, Zhong M, Niu B, Deng S, Zhu Z. A SVM-based system for predicting protein\u2013protein interactions using a novel representation of protein sequences. In: International conference on intelligent computing theories; 2013.","DOI":"10.1007\/978-3-642-39479-9_73"},{"issue":"6","key":"5594_CR31","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1109\/TCBB.2015.2401018","volume":"12","author":"B Sriwastava","year":"2015","unstructured":"Sriwastava B, Basu S, Maulik U. Predicting protein\u2013protein interaction sites with a novel membership based fuzzy SVM classifier. IEEE\/ACM Trans Comput Biol Bioinform. 2015;12(6):1394\u2013404.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"5594_CR32","doi-asserted-by":"crossref","unstructured":"Wong L, You ZH, Li S, Huang YA, Liu G. Detection of protein\u2013protein interactions from amino acid sequences using a rotation forest model with a novel PR-LPQ descriptor. In: International conference on intelligent computing; 2015.","DOI":"10.1007\/978-3-319-22053-6_75"},{"issue":"9","key":"5594_CR33","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.2174\/092986610791760306","volume":"17","author":"JL Yang","year":"2010","unstructured":"Yang JL. Prediction of protein\u2013protein interactions from protein sequence using local descriptors. Protein Peptide Lett. 2010;17(9):1085\u201390.","journal-title":"Protein Peptide Lett"},{"key":"5594_CR34","doi-asserted-by":"crossref","unstructured":"You ZH, Ming Z, Huang H, Peng X. A novel method to predict protein\u2013protein interactions based on the information of protein sequence. In: IEEE international conference on control system; 2013.","DOI":"10.1109\/ICCSCE.2012.6487143"},{"key":"5594_CR35","unstructured":"Minakuchi Y, Satou K, Konagaya A. Prediction of protein\u2013protein interaction sites using support vector machines. In: Proceedings of the international conference on mathematics and engineering techniques in medicine and biological sciences, METMBS \u201903, June 23\u201326, 2003, Las Vegas, Nevada, USA; 2003."},{"issue":"2","key":"5594_CR36","doi-asserted-by":"publisher","first-page":"467","DOI":"10.3390\/ijms21020467","volume":"21","author":"Z Xie","year":"2020","unstructured":"Xie Z, Deng X, Shu K. Prediction of protein\u2013protein interaction sites using convolutional neural network and improved data sets. Int J Mol Sci. 2020;21(2):467.","journal-title":"Int J Mol Sci"},{"key":"5594_CR37","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.jtbi.2018.10.029","volume":"461","author":"L Wang","year":"2018","unstructured":"Wang L, Yan X, Liu ML, Song KJ, Sun XF, Pan WW. Prediction of RNA\u2013protein interactions by combining deep convolutional neural network with feature selection ensemble method. J Theor Biol. 2018;461:230\u20138.","journal-title":"J Theor Biol"},{"key":"5594_CR38","unstructured":"Zhou J, Qin L, Xu R, Lin G, Wang H. CNNsite: prediction of DNA-binding residues in proteins using convolutional neural network with sequence features. In: IEEE international conference on bioinformatics and biomedicine; 2017."},{"issue":"24","key":"5594_CR39","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1093\/bioinformatics\/btab533","volume":"37","author":"X Yang","year":"2021","unstructured":"Yang X, Yang S, Lian X, Stefan W, Zhang Z. Transfer learning via multi-scale convolutional neural layers for human\u2013virus protein\u2013protein interaction prediction. Bioinformatics. 2021;37(24):24.","journal-title":"Bioinformatics"},{"issue":"24","key":"5594_CR40","doi-asserted-by":"publisher","first-page":"4668","DOI":"10.1093\/bioinformatics\/btab551","volume":"37","author":"H Yang","year":"2021","unstructured":"Yang H, Wang M, Liu X, Zhao XM, Li A. PhosIDN: an integrated deep neural network for improving protein phosphorylation site prediction by combining sequence and protein\u2013protein interaction information. Bioinformatics. 2021;37(24):4668\u201376.","journal-title":"Bioinformatics"},{"key":"5594_CR41","unstructured":"Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. 2016."},{"key":"5594_CR42","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0238915","volume":"15","author":"Z Xiao","year":"2020","unstructured":"Xiao Z, Deng Y. Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PLoS ONE. 2020;15: e0238915.","journal-title":"PLoS ONE"},{"key":"5594_CR43","doi-asserted-by":"crossref","unstructured":"Lv G, Hu Z, Bi Y, Zhang S. Learning unknown from correlations: graph neural network for inter-novel-protein interaction prediction. 2021.","DOI":"10.24963\/ijcai.2021\/506"},{"key":"5594_CR44","unstructured":"Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25(2)."},{"key":"5594_CR45","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Rabinovich A. Going deeper with convolutions. In: IEEE computer society; 2014.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"5594_CR46","unstructured":"Lin M, Chen Q, Yan S. Network in network. Comput Sci. 2013."},{"key":"5594_CR47","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. IEEE. 2016. p. 2818\u20132826.","DOI":"10.1109\/CVPR.2016.308"},{"key":"5594_CR48","unstructured":"Mnih V, Heess N, Graves A, Kavukcuoglu K. Recurrent models of visual attention. Adv Neural Inf Process Syst. 2014;3."},{"key":"5594_CR49","unstructured":"Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. Comput Sci. 2014."},{"key":"5594_CR50","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I. Attention is all you need. arXiv 2017."},{"key":"5594_CR51","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1093\/nar\/gkw937","volume":"45","author":"S Damian","year":"2017","unstructured":"Damian S, Morris JH, Helen C, Michael K, Stefan W, Milan S, Alberto S, Doncheva NT, Alexander R, Peer B. The string database in 2017: quality-controlled protein\u2013protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45:362\u20138.","journal-title":"Nucleic Acids Res"},{"issue":"14","key":"5594_CR52","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1093\/bioinformatics\/btz328","volume":"35","author":"M Chen","year":"2019","unstructured":"Chen M, Ju JT, Zhou G, Chen X, Wang W. Multifaceted protein\u2013protein interaction prediction based on siamese residual RCNN. Bioinformatics. 2019;35(14):305\u201314.","journal-title":"Bioinformatics"},{"issue":"17","key":"5594_CR53","first-page":"17","volume":"34","author":"H Somaye","year":"2018","unstructured":"Somaye H, Behnam N, Khan AA, Jinbo X. Predicting protein\u2013protein interactions through sequence-based deep learning. Bioinformatics. 2018;34(17):17.","journal-title":"Bioinformatics"},{"issue":"Database issue","key":"5594_CR54","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1093\/nar\/gkh086","volume":"32","author":"L Salwinski","year":"2004","unstructured":"Salwinski L, Miller CS, Smith AJ, Pettit FK, Eisenberg D. The database of interacting proteins: 2004 update. Nucleic Acids Res. 2004;32(Database issue):449\u201351.","journal-title":"Nucleic Acids Res"},{"issue":"5","key":"5594_CR55","doi-asserted-by":"publisher","first-page":"2699","DOI":"10.1093\/nar\/gky092","volume":"46","author":"Alexandre Renaux","year":"2018","unstructured":"Renaux Alexandre. Uniprot: the universal protein knowledgebase (vol 45, pg d158, 2017). Nucleic Acids Res. 2018;46(5):2699\u20132699.","journal-title":"Nucleic Acids Res"},{"key":"5594_CR56","unstructured":"Mikolov T, Sutskever I, Kai C, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. In: arXiv 2013."},{"key":"5594_CR57","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks?. 2018."},{"key":"5594_CR58","unstructured":"Socher R, Chen D, Manning CD, Ng AY. Reasoning with neural tensor networks for knowledge base completion. Curran Associates Inc. 2013."},{"issue":"8","key":"5594_CR59","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.3390\/molecules23081923","volume":"23","author":"L Hang","year":"2018","unstructured":"Hang L, Xiu-Jun G, Hua Y, Chang Z. Deep neural network based predictions of protein interactions using primary sequences. Molecules. 2018;23(8):1923.","journal-title":"Molecules"},{"issue":"2","key":"5594_CR60","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1093\/bib\/bbab558","volume":"23","author":"B Song","year":"2022","unstructured":"Song B, Luo X, Luo X, Liu Y, Niu Z, Zeng X. Learning spatial structures of proteins improves protein\u2013protein interaction prediction. Brief Bioinform. 2022;23(2):558. https:\/\/doi.org\/10.1093\/bib\/bbab558.","journal-title":"Brief Bioinform"},{"key":"5594_CR61","unstructured":"Kingma D, Ba J. Adam: a method for stochastic optimization. Comput Sci. 2014."}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05594-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-023-05594-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05594-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T13:12:13Z","timestamp":1702559533000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-023-05594-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,14]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["5594"],"URL":"https:\/\/doi.org\/10.1186\/s12859-023-05594-5","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,14]]},"assertion":[{"value":"8 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"473"}}