{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:44:14Z","timestamp":1753875854111,"version":"3.41.2"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T00:00:00Z","timestamp":1633392000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1611265","11631015","12026601","61976229"],"award-info":[{"award-number":["U1611265","11631015","12026601","61976229"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Learning node representation is a fundamental problem in biological network analysis, as compact representation features reveal complicated network structures and carry useful information for downstream tasks such as link prediction and node classification. Recently, multiple networks that profile objects from different aspects are increasingly accumulated, providing the opportunity to learn objects from multiple perspectives. However, the complex common and specific information across different networks pose challenges to node representation methods. Moreover, ubiquitous noise in networks calls for more robust representation. To deal with these problems, we present a representation learning method for multiple biological networks. First, we accommodate the noise and spurious edges in networks using denoised diffusion, providing robust connectivity structures for the subsequent representation learning. Then, we introduce a graph regularized integration model to combine refined networks and compute common representation features. By using the regularized decomposition technique, the proposed model can effectively preserve the common structural property of different networks and simultaneously accommodate their specific information, leading to a consistent representation. A simulation study shows the superiority of the proposed method on different levels of noisy networks. Three network-based inference tasks, including drug\u2013target interaction prediction, gene function identification and fine-grained species categorization, are conducted using representation features learned from our method. Biological networks at different scales and levels of sparsity are involved. Experimental results on real-world data show that the proposed method has robust performance compared with alternatives. Overall, by eliminating noise and integrating effectively, the proposed method is able to learn useful representations from multiple biological networks.<\/jats:p>","DOI":"10.1093\/bib\/bbab409","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T11:20:51Z","timestamp":1631100051000},"source":"Crossref","is-referenced-by-count":6,"title":["Learning representation for multiple biological networks via a robust graph regularized integration approach"],"prefix":"10.1093","volume":"23","author":[{"given":"Xiwen","family":"Zhang","sequence":"first","affiliation":[{"name":"Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, 510275, Guangzhou, China"}]},{"given":"Weiwen","family":"Wang","sequence":"additional","affiliation":[{"name":"Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, 510275, Guangzhou, China"}]},{"given":"Chuan-Xian","family":"Ren","sequence":"additional","affiliation":[{"name":"Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China, and Pazhou Lab, Guangzhou, 510330, China"}]},{"given":"Dao-Qing","family":"Dai","sequence":"additional","affiliation":[{"name":"Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, 510275, Guangzhou, China"}]}],"member":"286","published-online":{"date-parts":[[2021,10,5]]},"reference":[{"issue":"D1","key":"2022011921130329900_ref1","doi-asserted-by":"crossref","first-page":"D808","DOI":"10.1093\/nar\/gks1094","article-title":"String v9.1: Protein-protein interaction networks, with increased coverage and integration","volume":"41","author":"Franceschini","year":"2013","journal-title":"Nucleic Acids Res"},{"issue":"10","key":"2022011921130329900_ref2","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/S0168-9525(02)02763-4","article-title":"Bridging structural biology and genomics: Assessing protein interaction data with known complexes","volume":"18","author":"Edwards","year":"2002","journal-title":"Trends Genet"},{"key":"2022011921130329900_ref3","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1145\/2623330.2623732","volume-title":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD\u201914","author":"Perozzi","year":"2014"},{"issue":"12","key":"2022011921130329900_ref4","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1038\/82360","article-title":"A network of protein-protein interactions in yeast","volume":"18","author":"Schwikowski","year":"2000","journal-title":"Nat Biotechnol"},{"issue":"1","key":"2022011921130329900_ref5","doi-asserted-by":"crossref","first-page":"3108","DOI":"10.1038\/s41467-018-05469-x","article-title":"Network enhancement as a general method to denoise weighted biological networks","volume":"9","author":"Wang","year":"2018","journal-title":"Nat Commun"},{"issue":"3","key":"2022011921130329900_ref6","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/nmeth.2810","article-title":"Similarity network fusion for aggregating data types on a genomic scale","volume":"11","author":"Wang","year":"2014","journal-title":"Nat Methods"},{"issue":"2","key":"2022011921130329900_ref7","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1093\/bib\/bbz015","article-title":"Evaluation of integrative clustering methods for the analysis of multi-omics data","volume":"21","author":"Chauvel","year":"2020","journal-title":"Brief Bioinform"},{"issue":"1","key":"2022011921130329900_ref8","doi-asserted-by":"crossref","first-page":"1796","DOI":"10.1038\/s41467-021-21770-8","article-title":"Identification of disease treatment mechanisms through the multiscale interactome","volume":"12","author":"Ruiz","year":"2021","journal-title":"Nat Commun"},{"issue":"1","key":"2022011921130329900_ref9","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1093\/bib\/bby117","article-title":"Network embedding in biomedical data science","volume":"21","author":"Su","year":"2018","journal-title":"Brief Bioinform"},{"issue":"6887","key":"2022011921130329900_ref10","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1038\/nature750","article-title":"Comparative assessment of large-scale data sets of protein-protein interactions","volume":"417","author":"Mering","year":"2002","journal-title":"Nature"},{"issue":"4","key":"2022011921130329900_ref11","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1145\/279232.279236","article-title":"Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization","volume":"23","author":"Zhu","year":"1997","journal-title":"ACM Transactions on Mathematical Software"},{"issue":"3","key":"2022011921130329900_ref12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"Libsvm: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"issue":"10","key":"2022011921130329900_ref13","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1093\/bioinformatics\/btv026","article-title":"Topology-function conservation in protein-protein interaction networks","volume":"31","author":"Davis","year":"2015","journal-title":"Bioinformatics"},{"issue":"4","key":"2022011921130329900_ref14","doi-asserted-by":"crossref","first-page":"200","DOI":"10.5808\/GI.2013.11.4.200","article-title":"Review of biological network data and its applications","volume":"11","author":"Yu","year":"2013","journal-title":"Genomics & Informatics"},{"issue":"1","key":"2022011921130329900_ref15","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TBDATA.2018.2850013","article-title":"Network representation learning: A survey","volume":"6","author":"Zhang","year":"2020","journal-title":"IEEE Transactions on Big Data"},{"issue":"1","key":"2022011921130329900_ref16","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1093\/bioinformatics\/bty543","article-title":"Neodti: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions","volume":"35","author":"Wan","year":"2019","journal-title":"Bioinformatics"},{"key":"2022011921130329900_ref17","doi-asserted-by":"crossref","first-page":"24032","DOI":"10.1109\/ACCESS.2017.2766758","article-title":"Predicting microrna-disease associations using network topological similarity based on deepwalk","volume":"5","author":"Li","year":"2017","journal-title":"IEEE Access"},{"key":"2022011921130329900_ref18","first-page":"62","article-title":"Diffusion component analysis: Unraveling functional topology in biological networks","volume":"9029","author":"Cho","year":"2015","journal-title":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"2022011921130329900_ref19","first-page":"540","article-title":"Compact integration of multi-network topology for functional analysis of genes","volume-title":"Cell Systems","author":"Cho","year":"2016"},{"key":"2022011921130329900_ref20","first-page":"1067","volume-title":"Proc. ICWWW","author":"Tang","year":"2015"},{"key":"2022011921130329900_ref21","first-page":"2.1","volume-title":"Proceedings of the British Machine Vision Conference, BMVC","author":"Wang","year":"2009"},{"key":"2022011921130329900_ref22","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1186\/1471-2105-10-283","article-title":"Rrw: Repeated random walks on genome-scale protein networks for local cluster discovery","volume":"10","author":"Macropol","year":"2009","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"2022011921130329900_ref23","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: Tool for the unification of biology","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat Genet"},{"issue":"39","key":"2022011921130329900_ref24","doi-asserted-by":"crossref","first-page":"11853","DOI":"10.1021\/ja036030u","article-title":"Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways","volume":"125","author":"Hattori","year":"2003","journal-title":"J Am Chem Soc"},{"issue":"1","key":"2022011921130329900_ref25","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TPAMI.2014.2343973","article-title":"Data fusion by matrix factorization","volume":"37","author":"\u017ditnik","year":"2015","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"2022011921130329900_ref26","doi-asserted-by":"crossref","first-page":"e1004552","DOI":"10.1371\/journal.pcbi.1004552","article-title":"Gene prioritization by compressive data fusion and chaining","volume":"11","author":"\u017ditnik","year":"2015","journal-title":"PLoS Comput Biol"},{"issue":"13","key":"2022011921130329900_ref27","doi-asserted-by":"crossref","first-page":"i457","DOI":"10.1093\/bioinformatics\/bty294","article-title":"Modeling polypharmacy side effects with graph convolutional networks","volume":"34","author":"\u017ditnik","year":"2018","journal-title":"Bioinformatics"},{"key":"2022011921130329900_ref28","doi-asserted-by":"crossref","first-page":"3202","DOI":"10.1038\/srep03202","article-title":"Discovering disease-disease associations by fusing systems-level molecular data","volume":"3","author":"\u017ditnik","year":"2013","journal-title":"Sci Rep"},{"issue":"4","key":"2022011921130329900_ref29","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1093\/bioinformatics\/btx624","article-title":"Deepgo: Predicting protein functions from sequence and interactions using a deep ontology-aware classifier","volume":"34","author":"Kulmanov","year":"2018","journal-title":"Bioinformatics"},{"issue":"12","key":"2022011921130329900_ref30","doi-asserted-by":"crossref","first-page":"I60","DOI":"10.1093\/bioinformatics\/btu269","article-title":"Inductive matrix completion for predicting gene-disease associations","volume":"30","author":"Natarajan","year":"2014","journal-title":"Bioinformatics"},{"issue":"15","key":"2022011921130329900_ref31","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1093\/bioinformatics\/btx160","article-title":"Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations","volume":"33","author":"Zong","year":"2017","journal-title":"Bioinformatics"},{"issue":"3","key":"2022011921130329900_ref32","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1038\/nmeth.2340","article-title":"A large-scale evaluation of computational protein function prediction","volume":"10","author":"Radivojac","year":"2013","journal-title":"Nat Methods"},{"key":"2022011921130329900_ref33","first-page":"891","volume-title":"Proceedings of the 24th International Conference on Information and Knowledge Management, CIKM\u201915","author":"Cao","year":"2015"},{"issue":"12","key":"2022011921130329900_ref34","doi-asserted-by":"crossref","first-page":"i357","DOI":"10.1093\/bioinformatics\/btv260","article-title":"Exploiting ontology graph for predicting sparsely annotated gene function","volume":"31","author":"Wang","year":"2015","journal-title":"Bioinformatics"},{"key":"2022011921130329900_ref35","first-page":"2019","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR","author":"Berg","year":"2014"},{"issue":"1","key":"2022011921130329900_ref36","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/0022-2836(81)90087-5","article-title":"Identification of common molecular subsequences","volume":"147","author":"Smith","year":"1981","journal-title":"J Mol Biol"},{"key":"2022011921130329900_ref37","doi-asserted-by":"crossref","first-page":"275045","DOI":"10.1155\/2015\/275045","article-title":"Matrix factorization-based prediction of novel drug indications by integrating genomic space","volume":"2015","author":"Dai","year":"2015","journal-title":"Comput Math Methods Med"},{"issue":"MAY","key":"2022011921130329900_ref38","doi-asserted-by":"crossref","first-page":"381","DOI":"10.3389\/fgene.2019.00381","article-title":"To embed or not: Network embedding as a paradigm in computational biology","volume":"10","author":"Nelson","year":"2019","journal-title":"Front Genet"},{"key":"2022011921130329900_ref39","first-page":"52","article-title":"Representation learning on graphs: Methods and applications","volume":"40","author":"Hamilton","year":"2017","journal-title":"IEEE Data Engineering Bulletin"},{"issue":"4","key":"2022011921130329900_ref40","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1093\/bib\/bbv066","article-title":"Drug-target interaction prediction: Databases, web servers and computational models","volume":"17","author":"Chen","year":"2016","journal-title":"Brief Bioinform"},{"issue":"4","key":"2022011921130329900_ref41","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1093\/bioinformatics\/btz718","article-title":"Graph embedding on biomedical networks: Methods, applications and evaluations","volume":"36","author":"Yue","year":"2020","journal-title":"Bioinformatics"},{"key":"2022011921130329900_ref42","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1145\/2487575.2487670","volume-title":"Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201913","author":"Zheng","year":"2013"},{"issue":"2","key":"2022011921130329900_ref43","doi-asserted-by":"crossref","first-page":"e1004760","DOI":"10.1371\/journal.pcbi.1004760","article-title":"Neighborhood regularized logistic matrix factorization for drug-target interaction prediction","volume":"12","author":"Liu","year":"2016","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"2022011921130329900_ref44","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nat Commun"},{"issue":"13","key":"2022011921130329900_ref45","doi-asserted-by":"crossref","first-page":"i232","DOI":"10.1093\/bioinformatics\/btn162","article-title":"Prediction of drug-target interaction networks from the integration of chemical and genomic spaces","volume":"24","author":"Yamanishi","year":"2008","journal-title":"Bioinformatics"},{"issue":"12","key":"2022011921130329900_ref46","doi-asserted-by":"crossref","first-page":"i246","DOI":"10.1093\/bioinformatics\/btq176","article-title":"Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework","volume":"26","author":"Yamanishi","year":"2010","journal-title":"Bioinformatics"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab409\/42230968\/bbab409.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab409\/42230968\/bbab409.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T14:23:39Z","timestamp":1699453419000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab409\/6381251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,5]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1,17]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab409","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"type":"print","value":"1467-5463"},{"type":"electronic","value":"1477-4054"}],"subject":[],"published-other":{"date-parts":[[2022,1]]},"published":{"date-parts":[[2021,10,5]]},"article-number":"bbab409"}}