{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:24:35Z","timestamp":1774052675962,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"S16","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T00:00:00Z","timestamp":1575244800000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"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><jats:title>Background<\/jats:title><jats:p>Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>To the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-019-3084-y","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T12:00:35Z","timestamp":1575288035000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Multimodal deep representation learning for protein interaction identification and protein family classification"],"prefix":"10.1186","volume":"20","author":[{"given":"Da","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mansur","family":"Kabuka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,2]]},"reference":[{"issue":"9","key":"3084_CR1","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.2174\/092986610791760306","volume":"17","author":"L Yang","year":"2010","unstructured":"Yang L, Xia J-F, Gui J. Prediction of protein-protein interactions from protein sequence using local descriptors. Protein Pept Lett. 2010; 17(9):1085\u201390.","journal-title":"Protein Pept Lett"},{"key":"3084_CR2","first-page":"254","volume-title":"Communications in Computer and Information Science","author":"Yu Zhen Zhou","year":"2011","unstructured":"Zhou YZ, Gao Y, Zheng YY. Prediction of protein-protein interactions using local description of amino acid sequence. Advanc Comput Sci Educ Appl. 2011:254\u201362. https:\/\/doi.org\/10.1007\/978-3-642-22456-0_37."},{"issue":"9","key":"3084_CR3","doi-asserted-by":"publisher","first-page":"3025","DOI":"10.1093\/nar\/gkn159","volume":"36","author":"Y Guo","year":"2008","unstructured":"Guo Y, Yu L, Wen Z, Li M. Using support vector machine combined with auto covariance to predict protein\u2013protein interactions from protein sequences. Nucleic Acids Res. 2008; 36(9):3025\u201330.","journal-title":"Nucleic Acids Res"},{"issue":"8","key":"3084_CR4","doi-asserted-by":"publisher","first-page":"2093","DOI":"10.1099\/mic.0.26355-0","volume":"149","author":"E. A. Creasey","year":"2003","unstructured":"Creasey EA, Delahay R, Daniell SJ, Frankel G.Yeast two-hybrid system survey of interactions between lee-encoded proteins of enteropathogenic escherichia coli. Microbiology. 2003; 149(8):2093\u2013106. https:\/\/doi.org\/10.1099\/mic.0.26355-0.","journal-title":"Microbiology"},{"key":"3084_CR5","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1038\/415180a","volume":"6868","author":"Y Ho","year":"2002","unstructured":"Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams S-L, Millar A, et al.Systematic identification of protein complexes in saccharomyces cerevisiae by mass spectrometry. Nature. 2002; 6868:180.","journal-title":"Nature"},{"key":"3084_CR6","doi-asserted-by":"publisher","first-page":"23262","DOI":"10.1074\/jbc.M401932200","volume":"279","author":"M Bhasin","year":"2004","unstructured":"Bhasin M, Raghava GP. Classification of nuclear receptors based on amino acid composition and dipeptide composition. J Biol Chem. 2004; 279:23262\u20136.","journal-title":"J Biol Chem"},{"issue":"1","key":"3084_CR7","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1186\/1471-2105-11-175","volume":"11","author":"R Saidi","year":"2010","unstructured":"Saidi R, Maddouri M, Nguifo EM. Protein sequences classification by means of feature extraction with substitution matrices. BMC bioinformatics. 2010; 11(1):175.","journal-title":"BMC bioinformatics"},{"key":"3084_CR8","first-page":"436","volume":"7553","author":"L Yann","year":"2015","unstructured":"Yann L, Bengio Y, Hinton G. Deep learning. nature. 2015; 7553:436.","journal-title":"nature"},{"issue":"6","key":"3084_CR9","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(6):1499\u2013510.","journal-title":"J Chem Inf Model"},{"issue":"1","key":"3084_CR10","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 bioinformatics. 2017; 18(1):277.","journal-title":"BMC bioinformatics"},{"key":"3084_CR11","unstructured":"Lee TK, Nguyen T. Protein family classification with neural networks. 2016. https:\/\/cs224d.stanford.edu\/reports\/LeeNguyen.pdf."},{"key":"3084_CR12","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1109\/TCBB.2015.2394314","volume":"2","author":"W Peng","year":"2017","unstructured":"Peng W, Li M, Chen L, Wang L. Predicting protein functions by using unbalanced random walk algorithm on three biological networks. IEEE\/ACM Trans Comput Biol Bioinforma. 2017; 2:360\u20139.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma"},{"issue":"6","key":"3084_CR13","doi-asserted-by":"publisher","first-page":"0198216","DOI":"10.1371\/journal.pone.0198216","volume":"13","author":"R Fa","year":"2018","unstructured":"Fa R, Cozzetto D, Wan C, Jones DT. Predicting human protein function with multi-task deep neural networks. PloS one. 2018; 13(6):0198216.","journal-title":"PloS one"},{"key":"3084_CR14","first-page":"4","volume":"1","author":"Z Chen","year":"2018","unstructured":"Chen Z, Zhao P, Li F, Leier A, Marquez-Lago TT, Wang Y, Webb GI, et al.ifeature: a python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics. 2018; 1:4.","journal-title":"Bioinformatics"},{"issue":"13","key":"3084_CR15","doi-asserted-by":"publisher","first-page":"1780","DOI":"10.1093\/bioinformatics\/btr291","volume":"27","author":"TY Lee","year":"2011","unstructured":"Lee TY, Lin ZQ, Hsieh S-J, Breta\u00f1a NA, Lu C-T. Exploiting maximal dependence decomposition to identify conserved motifs from a group of aligned signal sequences. Bioinformatics. 2011; 27(13):1780\u20137.","journal-title":"Bioinformatics"},{"issue":"11","key":"3084_CR16","doi-asserted-by":"publisher","first-page":"4337","DOI":"10.1073\/pnas.0607879104","volume":"104","author":"J Shen","year":"2007","unstructured":"Shen J, Zhang J, 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. 2007; 104(11):4337\u201341.","journal-title":"Proc Natl Acad Sci"},{"key":"3084_CR17","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S. Deepwalk: Online learning of social representations. Proc 20th ACM SIGKDD Int Conf Knowl Discov Data Min. 2014:701\u201310. https:\/\/doi.org\/10.1145\/2623330.2623732.","DOI":"10.1145\/2623330.2623732"},{"key":"3084_CR18","doi-asserted-by":"crossref","unstructured":"Dong Y, Chawla NV, Swami A. metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM: 2017. p. 135\u201344.","DOI":"10.1145\/3097983.3098036"},{"issue":"2","key":"3084_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00433ED1V01Y201207DMK005","volume":"3","author":"Yizhou Sun","year":"2012","unstructured":"Sun Y, Han J. Mining heterogeneous information networks: principles and methodologies. Synth Lect Data Min Knowl Discov. 2012; 3(2):1\u2013159. https:\/\/doi.org\/10.2200\/s00433ed1v01y201207dmk005.","journal-title":"Synthesis Lectures on Data Mining and Knowledge Discovery"},{"key":"3084_CR20","unstructured":"Goyal P., Ferrara E. Graph embedding techniques, applications, and performance: A survey. arXiv. 2017; 1705.02801."},{"key":"3084_CR21","doi-asserted-by":"crossref","unstructured":"Cao S, Lu W, Xu Q. Deep neural networks for learning graph representations. In: Thirtieth AAAI Conference on Artificial Intelligence.2016.","DOI":"10.1609\/aaai.v30i1.10179"},{"issue":"5786","key":"3084_CR22","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006; 313(5786):504\u20137.","journal-title":"Science"},{"issue":"4","key":"3084_CR23","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1039\/c3mb70486f","volume":"10","author":"I Saha","year":"2014","unstructured":"Saha I, Zubek J, Klingstr\u00f6m T, Forsberg S, Wikander J, Kierczak M, Maulik U, Plewczynski D. Ensemble learning prediction of protein protein interactions using proteins functional annotations. Mol BioSyst. 2014; 10(4):820\u201330.","journal-title":"Mol BioSyst"},{"issue":"2","key":"3084_CR24","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1093\/bioinformatics\/bth483","volume":"21","author":"S Martin","year":"2004","unstructured":"Martin S, Diana Roe D, Faulon J-L. Predicting protein\u2013protein interactions using signature products. Bioinformatics. 2004; 21(2):218\u201326.","journal-title":"Bioinformatics"},{"issue":"1","key":"3084_CR25","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/1756-0500-3-145","volume":"3","author":"Y Guo","year":"2010","unstructured":"Guo Y, Li M, Pu X, Li G, Guang X, Xiong W, Li J. Pred_ppi: a server for predicting protein-protein interactions based on sequence data with probability assignment. BMC research notes. 2010; 3(1):145.","journal-title":"BMC research notes"},{"issue":"1","key":"3084_CR26","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3390\/ijms17010021","volume":"17","author":"L Wong","year":"2015","unstructured":"Wong L, You Z-H, Ming Z, Li J, Chen X, Huang Y-A. Detection of interactions between proteins through rotation forest and local phase quantization descriptors. Int J Mol Sci. 2015; 17(1):21.","journal-title":"Int J Mol Sci"},{"issue":"Suppl 15","key":"3084_CR27","doi-asserted-by":"publisher","first-page":"S9","DOI":"10.1186\/1471-2105-15-S15-S9","volume":"15","author":"Zhu-Hong You","year":"2014","unstructured":"You H, Zhu L, Zheng C-H, Yu H-J, Deng S-P, Ji Z. Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set. BMC Bioinformatics. 2014; 15(15). 2014;15(15).","journal-title":"BMC Bioinformatics"},{"key":"3084_CR28","first-page":"1","volume":"2015","author":"Yu-An Huang","year":"2015","unstructured":"Huang Y-A, You Z-H, Gao X, Wong L, Wang L. Using weighted sparse representation model combined with discrete cosine transformation to predict protein-protein interactions from protein sequence. BioMed Res Int. 2015. https:\/\/doi.org\/10.1155\/2015\/902198.","journal-title":"BioMed Research International"},{"key":"3084_CR29","doi-asserted-by":"crossref","unstructured":"Zhang D, Kabuka MR. Multimodal deep representation learning for protein-protein interaction networks. IEEE Int Conf Bioinforma Biomed. 2018; Madrid Spain. https:\/\/doi.org\/10.1109\/bibm.2018.8621366.","DOI":"10.1109\/BIBM.2018.8621366"},{"issue":"10","key":"3084_CR30","doi-asserted-by":"publisher","first-page":"4992","DOI":"10.1021\/pr100618t","volume":"9","author":"XY Pan","year":"2010","unstructured":"Pan XY, Zhang Y, Shen HB. Large scale prediction of human protein protein interactions from amino acid sequence based on latent topic features. J Proteome Res. 2010; 9(10):4992\u20135001.","journal-title":"J Proteome Res"},{"key":"3084_CR31","doi-asserted-by":"crossref","unstructured":"Nguyen N-P, Nute M, Mirarab S, Warnow T, genomics BMC. Hippi: highly accurate protein family classification with ensembles of hmms. 2016;:765. https:\/\/doi.org\/10.1186\/s12864-016-3097-0.","DOI":"10.1186\/s12864-016-3097-0"},{"key":"3084_CR32","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.ymeth.2017.06.034","volume":"132","author":"Bal\u00e1zs Szalkai","year":"2018","unstructured":"Szalkai B, Grolmusz V. Near perfect protein multi-label classification with deep neural networks. Methods. 2018;:50\u20136. https:\/\/doi.org\/10.1016\/j.ymeth.2017.06.034.","journal-title":"Methods"},{"key":"3084_CR33","doi-asserted-by":"crossref","unstructured":"Naveenkumar KS, Mohammed BR, Vinayakumar HR, Soman KP. Protein family classification with neural networks. bioRxiv. 2018;:414128.","DOI":"10.1101\/414128"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3084-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-019-3084-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3084-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T09:20:47Z","timestamp":1665134447000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-019-3084-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":33,"journal-issue":{"issue":"S16","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["3084"],"URL":"https:\/\/doi.org\/10.1186\/s12859-019-3084-y","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"2 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not 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":"531"}}