{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:36Z","timestamp":1772138016417,"version":"3.50.1"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,2,4]],"date-time":"2023-02-04T00:00:00Z","timestamp":1675468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Marie Sklodowska-Curie","award":["813533"],"award-info":[{"award-number":["813533"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Many problems in life sciences can be brought back to a comparison of graphs. Even though a multitude of such techniques exist, often, these assume prior knowledge about the partitioning or the number of clusters and fail to provide statistical significance of observed between-network heterogeneity. Addressing these issues, we developed an unsupervised workflow to identify groups of graphs from reliable network-based statistics. In particular, we first compute the similarity between networks via appropriate distance measures between graphs and use them in an unsupervised hierarchical algorithm to identify classes of similar networks. Then, to determine the optimal number of clusters, we recursively test for distances between two groups of networks. The test itself finds its inspiration in distance-wise ANOVA algorithms. Finally, we assess significance via the permutation of between-object distance matrices. Notably, the approach, which we will call netANOVA, is flexible since users can choose multiple options to adapt to specific contexts and network types. We demonstrate the benefits and pitfalls of our approach via extensive simulations and an application to two real-life datasets. NetANOVA achieved high performance in many simulation scenarios while controlling type I error. On non-synthetic data, comparison against state-of-the-art methods showed that netANOVA is often among the top performers. There are many application fields, including precision medicine, for which identifying disease subtypes via individual-level biological networks improves prevention programs, diagnosis and disease monitoring.<\/jats:p>","DOI":"10.1093\/bib\/bbad029","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T16:31:41Z","timestamp":1673541101000},"source":"Crossref","is-referenced-by-count":3,"title":["netANOVA: novel graph clustering technique with significance assessment via hierarchical ANOVA"],"prefix":"10.1093","volume":"24","author":[{"given":"Diane","family":"Duroux","sequence":"first","affiliation":[{"name":"BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege , Liege , Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristel","family":"Van Steen","sequence":"additional","affiliation":[{"name":"BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege , Liege , Belgium"},{"name":"BIO3 - Systems Medicine, Department of Human Genetics , KU Leuven, Leuven , Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,2,4]]},"reference":[{"key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.3389\/fgene.2019.01381","article-title":"Heterogeneous multi-layered network model for omics data integration and analysis","volume":"10","author":"Lee","year":"2020","journal-title":"Front Genet"},{"issue":"2","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1093\/bib\/bbaa257","article-title":"Biological network analysis with deep learning","volume":"22","author":"Muzio","year":"2021","journal-title":"Brief Bioinform"},{"issue":"03","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1940007","DOI":"10.1142\/S0219720019400079","article-title":"Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles","volume":"17","author":"Matsubara","year":"2019","journal-title":"J Bioinform Comput Biol"},{"key":"2023032004371222900_","author":"Rhee","year":"2017"},{"issue":"1","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12874-022-01544-6","article-title":"Individual-specific networks for prediction modelling \u2013 a scoping review of methods","volume":"22","author":"Gregorich","year":"2022","journal-title":"BMC Med Res Methodol"},{"issue":"7","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1016\/j.cell.2018.05.015","article-title":"Next-generation machine learning for biological networks","volume":"173","author":"Camacho","year":"2018","journal-title":"Cell"},{"issue":"6","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"e1000100","DOI":"10.1371\/journal.pcbi.1000100","article-title":"Network analysis of intrinsic functional brain connectivity in Alzheimer\u2019s disease","volume":"4","author":"Supekar","year":"2008","journal-title":"PLoS Comput Biol"},{"issue":"4","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1093\/brain\/awn018","article-title":"Disrupted small-world networks in schizophrenia","volume":"131","author":"Liu","year":"2008","journal-title":"Brain"},{"issue":"1","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-53708-y","article-title":"Comparing methods for comparing networks","volume":"9","author":"Tantardini","year":"2019","journal-title":"Sci Rep"},{"key":"2023032004371222900_","author":"Borgwardt","year":"2020"},{"key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: a review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"2023032004371222900_","author":"Sun","year":"2019"},{"key":"2023032004371222900_","author":"Bandyopadhyay","year":"2020"},{"key":"2023032004371222900_","author":"Narayanan","year":"2017"},{"key":"2023032004371222900_","first-page":"29","author":"Defferrard","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"2023032004371222900_","author":"Kipf","year":"2016"},{"issue":"7","key":"2023032004371222900_","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007084","article-title":"Machine and deep learning meet genome-scale metabolic modeling","volume":"15","author":"Zampieri","year":"2019","journal-title":"PLoS Comput Biol"},{"issue":"6","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1093\/bib\/bbx044","article-title":"Deep learning for healthcare: review, opportunities and challenges","volume":"19","author":"Miotto","year":"2018","journal-title":"Brief Bioinform"},{"key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1145\/2939672.2939778","volume-title":"Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","author":"Ribeiro","year":"2016"},{"key":"2023032004371222900_","volume-title":"Biomedical data privacy: problems, perspectives, and recent advances","author":"Malin","year":"2013"},{"key":"2023032004371222900_","first-page":"2014","volume-title":"International conference on machine learning PMLR","author":"Niepert","year":"2016"},{"issue":"8","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","article-title":"Analyzing learned molecular representations for property prediction","volume":"59","author":"Yang","year":"2019","journal-title":"J Chem Inf Model"},{"key":"2023032004371222900_","author":"Nouranizadeh","year":"2021"},{"key":"2023032004371222900_","author":"Wu","year":"2022"},{"issue":"1","key":"2023032004371222900_","first-page":"32","article-title":"A new method for non-parametric multivariate analysis of variance","volume":"26","author":"Anderson","year":"2001","journal-title":"Austral Ecol"},{"issue":"7","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"5309","DOI":"10.1007\/s10462-020-09821-w","article-title":"An overview of distance and similarity functions for structured data","volume":"53","author":"Onta\u00f1\u00f3n","year":"2020","journal-title":"Artif Intell Rev"},{"key":"2023032004371222900_","author":"Phillips","year":"2011"},{"key":"2023032004371222900_","first-page":"121","volume-title":"International Workshop on Graph-Based Representations in Pattern Recognition Springer","author":"Bai","year":"2013"},{"key":"2023032004371222900_","first-page":"29","author":"Kondor","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"2023032004371222900_","first-page":"2595","article-title":"In IJCAI","author":"Nikolentzos","year":"2018"},{"key":"2023032004371222900_","first-page":"488","volume-title":"Artificial intelligence and statistics PMLR","author":"Shervashidze","year":"2009"},{"key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1145\/2783258.2783417","volume-title":"Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining","author":"Yanardag","year":"2015"},{"key":"2023032004371222900_","first-page":"22","author":"Shervashidze","year":"2009","journal-title":"Advances in neural information processing systems"},{"key":"2023032004371222900_","first-page":"419","volume-title":"In 2013 IEEE Global Conference on Signal and Information Processing IEEE","author":"Hammond","year":"2013"},{"key":"2023032004371222900_","first-page":"28","author":"Sugiyama","year":"2015","journal-title":"Advances in neural information processing systems"},{"key":"2023032004371222900_","first-page":"162","volume-title":"Proceedings of the 2013 SIAM International Conference on Data Mining SIAM","author":"Koutra","year":"2013"},{"key":"2023032004371222900_","first-page":"451","article-title":"In BIOCOMP","author":"Yip","year":"2006"},{"issue":"3","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v079.i03","article-title":"Kernel-based regularized least squares inR(KRLS) andStata(krls)","volume":"79","author":"Ferwerda","year":"2017","journal-title":"J Stat Softw"},{"issue":"2","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1002\/j.1538-7305.1950.tb00463.x","article-title":"Error detecting and error correcting codes","volume":"29","author":"Hamming","year":"1950","journal-title":"Bell Syst Tech J"},{"key":"2023032004371222900_","first-page":"8","volume-title":"Fifth IEEE international conference on data mining (ICDM\u201905) IEEE","author":"Borgwardt","year":"2005"},{"issue":"2","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1093\/biomet\/asn007","article-title":"Hierarchical testing of variable importance","volume":"95","author":"Meinshausen","year":"2008","journal-title":"Biometrika"},{"key":"2023032004371222900_","article-title":"ANOVA: repeated measures, number 84sage","author":"Girden","year":"1992"},{"key":"2023032004371222900_","author":"Gao","year":"2020"},{"issue":"4","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1824777.1824779","article-title":"Clustering for metric and nonmetric distance measures","volume":"6","author":"Ackermann","year":"2010","journal-title":"ACM Trans Algorithms"},{"key":"2023032004371222900_","first-page":"1695","author":"Csardi","year":"2006","journal-title":"Int J Complex Syst"},{"issue":"2","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1021\/jm00106a046","article-title":"Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity","volume":"34","author":"Debnath","year":"1991","journal-title":"J Med Chem"},{"issue":"3","key":"2023032004371222900_","first-page":"1648","volume":"13","author":"Reli\u00f3n","year":"2019","journal-title":"Ann Appl Stat"},{"key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3389\/fncom.2018.00095","article-title":"Brain network analysis and classification based on convolutional neural network","volume":"12","author":"Meng","year":"2018","journal-title":"Front Comput Neurosci"},{"issue":"4","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s12021-017-9338-9","article-title":"Multimodal neuroimaging in schizophrenia: description and dissemination","volume":"15","author":"Aine","year":"2017","journal-title":"Neuroinformatics"},{"issue":"4","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/j.neuron.2011.09.006","article-title":"Functional network Organization of the Human Brain","volume":"72","author":"Power","year":"2011","journal-title":"Neuron"},{"issue":"2","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"e0228728","DOI":"10.1371\/journal.pone.0228728","article-title":"Metrics for graph comparison: a practitioner\u2019s guide","volume":"15","author":"Wills","year":"2020","journal-title":"PloS one"},{"key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"573","DOI":"10.3389\/fnhum.2013.00573","article-title":"Intrinsic functional network organization in high-functioning adolescents with autism spectrum disorder","volume":"7","author":"Redcay","year":"2013","journal-title":"Front Hum Neurosci"},{"key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"205","DOI":"10.3389\/fpsyt.2016.00205","article-title":"Resting-state functional connectivity in autism Spectrum disorders: a review","volume":"7","author":"Hull","year":"2017","journal-title":"Front Psych"},{"key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.procs.2019.11.146","article-title":"Dealing with noise problem in machine learning data-sets: a systematic review","volume":"161","author":"Gupta","year":"2019","journal-title":"Procedia Comput Sci"},{"key":"2023032004371222900_","article-title":"arXiv preprint arXiv:1809.10341","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"2023032004371222900_","doi-asserted-by":"crossref","DOI":"10.21236\/ADA164453","volume-title":"Learning internal representations by error propagation Technical report California Univ San Diego La Jolla Inst for Cognitive Science","author":"Rumelhart","year":"1985"},{"key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/978-3-540-45167-9_11","volume-title":"On graph kernels: Hardness results and efficient alternatives In Learning theory and kernel machines","author":"G\u00e4rtner","year":"2003"},{"issue":"1","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J Stat Softw"},{"key":"2023032004371222900_","volume-title":"Advances in neural information processing systems Citeseer","author":"Zhu","year":"2003"},{"issue":"7","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1109\/TPAMI.2012.235","article-title":"Graph classification using signal-subgraphs: applications in statistical Connectomics","volume":"35","author":"Vogelstein","year":"2013","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2023032004371222900_","first-page":"2186","volume-title":"International conference on machine learning PMLR","author":"Ivanov","year":"2018"},{"key":"2023032004371222900_","author":"Bai","year":"2019"},{"issue":"1","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-23152-5","article-title":"An ANOVA approach for statistical comparisons of brain networks","volume":"8","author":"Fraiman","year":"2018","journal-title":"Sci Rep"},{"issue":"3","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1111\/biom.12647","article-title":"Statistical significance for hierarchical clustering","volume":"73","author":"Kimes","year":"2017","journal-title":"Biometrics"},{"issue":"12","key":"2023032004371222900_","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1093\/bioinformatics\/btl117","article-title":"Pvclust: an R package for assessing the uncertainty in hierarchical clustering","volume":"22","author":"Suzuki","year":"2006","journal-title":"Bioinformatics"},{"issue":"1","key":"2023032004371222900_","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1005305","article-title":"A topological criterion for filtering information in complex brain networks","volume":"13","author":"De Vico Fallani","year":"2017","journal-title":"PLoS Comput Biol"},{"key":"2023032004371222900_","author":"Duroux","year":"2020"},{"key":"2023032004371222900_","author":"Rossi","year":"2015"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/2\/bbad029\/49560497\/bbad029.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/2\/bbad029\/49560497\/bbad029.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T19:07:41Z","timestamp":1679339261000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad029\/7026011"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,4]]},"references-count":68,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,3,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad029","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.06.28.497741","asserted-by":"object"}]},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,3]]},"published":{"date-parts":[[2023,2,4]]},"article-number":"bbad029"}}