{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T17:01:00Z","timestamp":1783530060049,"version":"3.55.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,4,26]],"date-time":"2016-04-26T00:00:00Z","timestamp":1461628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2016,4,26]],"date-time":"2016-04-26T00:00:00Z","timestamp":1461628800000},"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":["61373086"],"award-info":[{"award-number":["61373086"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"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>Background<\/jats:title>\n                <jats:p>Proteins are the important molecules which participate in virtually every aspect of cellular function within an organism in pairs. Although high-throughput technologies have generated considerable protein-protein interactions (PPIs) data for various species, the processes of experimental methods are both time-consuming and expensive. In addition, they are usually associated with high rates of both false positive and false negative results. Accordingly, a number of computational approaches have been developed to effectively and accurately predict protein interactions. However, most of these methods typically perform worse when other biological data sources (e.g., protein structure information, protein domains, or gene neighborhoods information) are not available. Therefore, it is very urgent to develop effective computational methods for prediction of PPIs solely using protein sequence information.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, we present a novel computational model combining weighted sparse representation based classifier (WSRC) and global encoding (GE) of amino acid sequence. Two kinds of protein descriptors, composition and transition, are extracted for representing each protein sequence. On the basis of such a feature representation, novel weighted sparse representation based classifier is introduced to predict protein interaction class. When the proposed method was evaluated with the PPIs data of <jats:italic>S. cerevisiae<\/jats:italic>, <jats:italic>Human<\/jats:italic> and <jats:italic>H. pylori<\/jats:italic>, it achieved high prediction accuracies of 96.82, 97.66 and 92.83\u00a0% respectively. Extensive experiments were performed for cross-species PPIs prediction and the prediction accuracies were also very promising.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>To further evaluate the performance of the proposed method, we then compared its performance with the method based on support vector machine (SVM). The results show that the proposed method achieved a significant improvement. Thus, the proposed method is a very efficient method to predict PPIs and may be a useful supplementary tool for future proteomics studies.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-016-1035-4","type":"journal-article","created":{"date-parts":[[2016,4,26]],"date-time":"2016-04-26T01:39:39Z","timestamp":1461634779000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":129,"title":["Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding"],"prefix":"10.1186","volume":"17","author":[{"given":"Yu-An","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhu-Hong","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keith","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2016,4,26]]},"reference":[{"issue":"8","key":"1035_CR1","doi-asserted-by":"publisher","first-page":"4569","DOI":"10.1073\/pnas.061034498","volume":"98","author":"T Ito","year":"2001","unstructured":"Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci. 2001;98(8):4569\u201374.","journal-title":"Proc Natl Acad Sci"},{"issue":"2","key":"1035_CR2","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1002\/prot.10074","volume":"47","author":"F Pazos","year":"2002","unstructured":"Pazos F, Valencia A. In silico two\u2010hybrid system for the selection of physically interacting protein pairs. Proteins: Struct, Funct, Bioinf. 2002;47(2):219\u201327.","journal-title":"Proteins: Struct, Funct, Bioinf"},{"issue":"6868","key":"1035_CR3","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1038\/415141a","volume":"415","author":"AC Gavin","year":"2002","unstructured":"Gavin AC, B\u00f6sche M, Krause R, Grandi P, Marzioch M, Bauer A, Schultz J, Rick JM, Michon AM, Cruciat CM. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature. 2002;415(6868):141\u20137.","journal-title":"Nature"},{"issue":"6868","key":"1035_CR4","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1038\/415180a","volume":"415","author":"Y Ho","year":"2002","unstructured":"Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams SL, Millar A, Taylor P, Bennett K, Boutilier K. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature. 2002;415(6868):180\u20133.","journal-title":"Nature"},{"issue":"1","key":"1035_CR5","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/S0014-5793(01)03293-8","volume":"513","author":"A Zanzoni","year":"2002","unstructured":"Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G. MINT: a Molecular INTeraction database. FEBS Lett. 2002;513(1):135\u201340.","journal-title":"FEBS Lett"},{"issue":"1","key":"1035_CR6","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1093\/nar\/gkg056","volume":"31","author":"GD Bader","year":"2003","unstructured":"Bader GD, Betel D, Hogue CW. BIND: the biomolecular interaction network database. Nucleic Acids Res. 2003;31(1):248\u201350.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"1035_CR7","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1093\/nar\/28.1.289","volume":"28","author":"I Xenarios","year":"2000","unstructured":"Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D. DIP: the database of interacting proteins. Nucleic Acids Res. 2000;28(1):289\u201391.","journal-title":"Nucleic Acids Res"},{"key":"1035_CR8","series-title":"Natural computation, 2008 ICNC'08 fourth international conference","first-page":"100","volume-title":"Prediction of protein-protein interactions from secondary structures in binding motifs using the statistic method","author":"JT Yu","year":"2008","unstructured":"Yu JT, Guo MZ. Prediction of protein-protein interactions from secondary structures in binding motifs using the statistic method, Natural computation, 2008 ICNC'08 fourth international conference. Shandong: IEEE; 2008. p. 100\u20133."},{"key":"1035_CR9","series-title":"Biomedical engineering and biotechnology (iCBEB), 2012 international conference","first-page":"413","volume-title":"Prediction of protein-protein interactions in saccharomyces cerevisiae based on protein secondary structure","author":"L Cai","year":"2012","unstructured":"Cai L, Pei Z, Qin S, Zhao X. Prediction of protein-protein interactions in saccharomyces cerevisiae based on protein secondary structure, Biomedical engineering and biotechnology (iCBEB), 2012 international conference. Macau: IEEE; 2012. p. 413\u20136."},{"issue":"3","key":"1035_CR10","doi-asserted-by":"publisher","first-page":"3650","DOI":"10.3390\/ijms13033650","volume":"13","author":"X Zhao","year":"2012","unstructured":"Zhao X, Li J, Huang Y, Ma Z, Yin M. Prediction of bioluminescent proteins using auto covariance transformation of evolutional profiles. Int J Mol Sci. 2012;13(3):3650\u201360.","journal-title":"Int J Mol Sci"},{"issue":"22","key":"1035_CR11","doi-asserted-by":"publisher","first-page":"5321","DOI":"10.1016\/j.febslet.2006.08.086","volume":"580","author":"N Liu","year":"2006","unstructured":"Liu N, Wang T. Protein-based phylogenetic analysis by using hydropathy profile of amino acids. FEBS Lett. 2006;580(22):5321\u20137.","journal-title":"FEBS Lett"},{"issue":"11","key":"1035_CR12","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"},{"issue":"4","key":"1035_CR13","first-page":"403","volume":"26","author":"W Meng","year":"2008","unstructured":"Meng W, Wang FF, Peng XJ, Shen CY, Wang YF. Prediction of protein\u2013protein interaction sites using support vector machine. J Appl Sci. 2008;26(4):403\u20138.","journal-title":"J Appl Sci"},{"issue":"1","key":"1035_CR14","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1186\/1471-2105-8-147","volume":"8","author":"Q Dong","year":"2007","unstructured":"Dong Q, Wang X, Lin L, Guan Y. Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins. BMC Bioinf. 2007;8(1):147.","journal-title":"BMC Bioinf"},{"issue":"1","key":"1035_CR15","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1002\/prot.20514","volume":"61","author":"H Chen","year":"2005","unstructured":"Chen H, Zhou HX. Prediction of interface residues in protein\u2013protein complexes by a consensus neural network method: test against NMR data. Proteins: Struct, Funct, Bioinf. 2005;61(1):21\u201335.","journal-title":"Proteins: Struct, Funct, Bioinf"},{"key":"1035_CR16","doi-asserted-by":"crossref","unstructured":"Chen X, Yan CC, Zhang X, You Z-H, Deng L, Liu Y, Zhang Y, Dai Q: WBSMDA: Within and Between Score for MiRNA-Disease Association prediction. Scientific reports 2016, 6.","DOI":"10.1038\/srep21106"},{"key":"1035_CR17","doi-asserted-by":"crossref","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. International journal of molecular sciences 2015, 17(1):21.","DOI":"10.3390\/ijms17010021"},{"key":"1035_CR18","doi-asserted-by":"crossref","unstructured":"You Z-H, Chan KC, Hu P: Predicting Protein-Protein Interactions from Primary Protein Sequences Using a Novel Multi-Scale Local Feature Representation Scheme and the Random Forest. PLoS One 2015, 10(5):e0125811.","DOI":"10.1371\/journal.pone.0125811"},{"key":"1035_CR19","doi-asserted-by":"crossref","unstructured":"Luo X, Ming Z, You Z, Li S, Xia Y, Leung H: Improving network topology-based protein interactome mapping via collaborative filtering. Knowledge-Based Systems 2015, 90:23-32.","DOI":"10.1016\/j.knosys.2015.10.003"},{"key":"1035_CR20","doi-asserted-by":"crossref","unstructured":"You Z-H, Lei Y-K, Gui J, Huang D-S, Zhou X: Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data. Bioinformatics 2010, 26(21):2744-2751.","DOI":"10.1093\/bioinformatics\/btq510"},{"key":"1035_CR21","doi-asserted-by":"crossref","unstructured":"You Z-H, Lei Y-K, Zhu L, Xia J, Wang B: Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC bioinformatics 2013, 14(Suppl8):S10.","DOI":"10.1186\/1471-2105-14-S8-S10"},{"key":"1035_CR22","doi-asserted-by":"crossref","unstructured":"You Z-H, Li J, Gao X, He Z, Zhu L, Lei Y-K, Ji Z: Detecting protein-protein interactions with a novel matrixbased protein sequence representation and support vector machines. BioMed research international 2015, 2015:1.","DOI":"10.1155\/2015\/867516"},{"key":"1035_CR23","doi-asserted-by":"crossref","unstructured":"Lei Y-K, You Z-H, Ji Z, Zhu L, Huang D-S: Assessing and predicting protein interactions by combining manifold embedding with multiple information integration. BMC bioinformatics 2012, 13(Suppl 7):S3.","DOI":"10.1186\/1471-2105-13-S7-S3"},{"key":"1035_CR24","doi-asserted-by":"crossref","unstructured":"You Z-H, Yin Z, Han K, Huang D-S, Zhou X: A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network. Bmc Bioinformatics 2010, 11(1):343.","DOI":"10.1186\/1471-2105-11-343"},{"key":"1035_CR25","doi-asserted-by":"crossref","unstructured":"Zhu L, You Z-H, Huang D-S: Increasing the reliability of protein\u2013protein interaction networks via non-convex semantic embedding. Neurocomputing 2013, 121:99-107.","DOI":"10.1016\/j.neucom.2013.04.027"},{"key":"1035_CR26","doi-asserted-by":"crossref","unstructured":"You ZH, Li S, Gao X, Luo X, Ji Z: Large-Scale Protein-Protein Interactions Detection by Integrating Big Biosensing Data with Computational Model. Biomed Research International 2014, 2014:598129-598129.","DOI":"10.1155\/2014\/598129"},{"key":"1035_CR27","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.neucom.2014.05.072","volume":"145","author":"ZH You","year":"2014","unstructured":"You ZH, Yu JZ, Zhu L, Li S, Wen ZK. A MapReduce based parallel SVM for large-scale predicting protein\u2013protein interactions. Neurocomputing. 2014;145:37\u201343.","journal-title":"Neurocomputing"},{"issue":"2","key":"1035_CR28","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1093\/bioinformatics\/bth483","volume":"21","author":"S Martin","year":"2005","unstructured":"Martin S, Roe D, Faulon JL. Predicting protein\u2013protein interactions using signature products. Bioinformatics. 2005;21(2):218\u201326.","journal-title":"Bioinformatics"},{"issue":"15","key":"1035_CR29","doi-asserted-by":"publisher","first-page":"1875","DOI":"10.1093\/bioinformatics\/btm270","volume":"23","author":"JA Capra","year":"2007","unstructured":"Capra JA, Singh M. Predicting functionally important residues from sequence conservation. Bioinformatics. 2007;23(15):1875\u201382.","journal-title":"Bioinformatics"},{"key":"1035_CR30","series-title":"Advances in natural computation","first-page":"1164","volume-title":"A new encoding scheme to improve the performance of protein structural class prediction","author":"ZH Zhang","year":"2005","unstructured":"Zhang ZH, Wang ZH, Wang YX. A new encoding scheme to improve the performance of protein structural class prediction, Advances in natural computation. Berlin: Springer; 2005. p. 1164\u201373."},{"issue":"2","key":"1035_CR31","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","volume":"31","author":"J Wright","year":"2009","unstructured":"Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y. Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell. 2009;31(2):210\u201327.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"1035_CR32","doi-asserted-by":"publisher","first-page":"5406","DOI":"10.1109\/TIT.2006.885507","volume":"52","author":"EJ Candes","year":"2006","unstructured":"Candes EJ, Tao T. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans Inf Theory. 2006;52(12):5406\u201325.","journal-title":"IEEE Trans Inf Theory"},{"issue":"1","key":"1035_CR33","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1137\/S1064827596304010","volume":"20","author":"SS Chen","year":"1998","unstructured":"Chen SS, Donoho DL, Saunders MA. Atomic decomposition by basis pursuit. SIAM J Sci Comput. 1998;20(1):33\u201361.","journal-title":"SIAM J Sci Comput"},{"issue":"2","key":"1035_CR34","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.jvcir.2012.05.003","volume":"24","author":"CY Lu","year":"2013","unstructured":"Lu CY, Min H, Gui J, Zhu L, Lei YK. Face recognition via weighted sparse representation. J Vis Commun Image Represent. 2013;24(2):111\u20136.","journal-title":"J Vis Commun Image Represent"},{"issue":"9","key":"1035_CR35","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"},{"key":"1035_CR36","series-title":"Advances in Computer Science and Education Applications","first-page":"254","volume-title":"Prediction of protein-protein interactions using local description of amino acid sequence","author":"YZ Zhou","year":"2011","unstructured":"Zhou YZ, Gao Y, Zheng YY. Prediction of protein-protein interactions using local description of amino acid sequence, Advances in Computer Science and Education Applications. Berlin: Springer; 2011. p. 254\u201362."},{"issue":"9","key":"1035_CR37","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.2174\/092986610791760306","volume":"17","author":"L Yang","year":"2010","unstructured":"Yang L, Xia JF, 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"},{"issue":"1","key":"1035_CR38","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1093\/bioinformatics\/19.1.125","volume":"19","author":"JR Bock","year":"2003","unstructured":"Bock JR, Gough DA. Whole-proteome interaction mining. Bioinformatics. 2003;19(1):125\u201334.","journal-title":"Bioinformatics"},{"issue":"1","key":"1035_CR39","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.neucom.2005.05.007","volume":"69","author":"L Nanni","year":"2005","unstructured":"Nanni L. Hyperplanes for predicting protein\u2013protein interactions. Neurocomputing. 2005;69(1):257\u201363.","journal-title":"Neurocomputing"},{"issue":"10","key":"1035_CR40","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1093\/bioinformatics\/btl055","volume":"22","author":"L Nanni","year":"2006","unstructured":"Nanni L, Lumini A. An ensemble of K-local hyperplanes for predicting protein\u2013protein interactions. Bioinformatics. 2006;22(10):1207\u201310.","journal-title":"Bioinformatics"},{"issue":"3","key":"1035_CR41","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s00726-009-0295-y","volume":"38","author":"MG Shi","year":"2010","unstructured":"Shi MG, Xia JF, Li XL, Huang DS. Predicting protein\u2013protein interactions from sequence using correlation coefficient and high-quality interaction dataset. Amino Acids. 2010;38(3):891\u20139.","journal-title":"Amino Acids"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-016-1035-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-016-1035-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-016-1035-4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-016-1035-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T18:25:19Z","timestamp":1706811919000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-016-1035-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,4,26]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,12]]}},"alternative-id":["1035"],"URL":"https:\/\/doi.org\/10.1186\/s12859-016-1035-4","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,4,26]]},"assertion":[{"value":"21 July 2015","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2016","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2016","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"184"}}