{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:10:07Z","timestamp":1750119007241,"version":"3.41.0"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"content-version":"am","delay-in-days":328,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["FA8650-18-2-7834","N66001-14-1-4028","N66001-15-C-4041","FA8750-17-2-0112"],"award-info":[{"award-number":["FA8650-18-2-7834","N66001-14-1-4028","N66001-15-C-4041","FA8750-17-2-0112"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K01 AG041211","P41 EB015897","R56 AG057895","R01 MH120482","S10 OD010683"],"award-info":[{"award-number":["K01 AG041211","P41 EB015897","R56 AG057895","R01 MH120482","S10 OD010683"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-1921310","DMS-2113099"],"award-info":[{"award-number":["DMS-1921310","DMS-2113099"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition Letters"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1016\/j.patrec.2024.06.011","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T16:19:58Z","timestamp":1718727598000},"page":"97-102","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Discovering the signal subgraph: An iterative screening approach on graphs"],"prefix":"10.1016","volume":"184","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1030-1432","authenticated-orcid":false,"given":"Cencheng","family":"Shen","sequence":"first","affiliation":[]},{"given":"Shangsi","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6621-4560","authenticated-orcid":false,"given":"Alexandra","family":"Badea","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0139-7201","authenticated-orcid":false,"given":"Carey E.","family":"Priebe","sequence":"additional","affiliation":[]},{"given":"Joshua T.","family":"Vogelstein","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.patrec.2024.06.011_b1","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1177\/016555150202800601","article-title":"Social network analysis: a powerful strategy, also for the information sciences","volume":"28","author":"Otte","year":"2002","journal-title":"J. Inf. Sci."},{"key":"10.1016\/j.patrec.2024.06.011_b2","doi-asserted-by":"crossref","first-page":"2566","DOI":"10.1073\/pnas.012582999","article-title":"Random graph models of social networks","volume":"99","author":"Newman","year":"2002","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"10.1016\/j.patrec.2024.06.011_b3","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1146\/annurev-clinpsy-040510-143934","article-title":"Brain graphs: graphical models of the human brain connectome","volume":"7","author":"Bullmore","year":"2011","journal-title":"Annu. Rev. Clin. Psychol."},{"key":"10.1016\/j.patrec.2024.06.011_b4","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":"10.1016\/j.patrec.2024.06.011_b5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.patrec.2017.04.005","article-title":"Manifold matching using shortest-path distance and joint neighborhood selection","volume":"92","author":"Shen","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patrec.2024.06.011_b6","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1093\/biomet\/asz045","article-title":"Network dependence testing via diffusion maps and distance-based correlations","volume":"106","author":"Lee","year":"2019","journal-title":"Biometrika"},{"key":"10.1016\/j.patrec.2024.06.011_b7","series-title":"Advances in Neural Information Processing Systems","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","author":"Hu","year":"2020"},{"key":"10.1016\/j.patrec.2024.06.011_b8","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"10.1016\/j.patrec.2024.06.011_b9","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1198\/106186006X113430","article-title":"Sparse principal component analysis","volume":"15","author":"Zou","year":"2006","journal-title":"J. Comput. Graph. Statist."},{"key":"10.1016\/j.patrec.2024.06.011_b10","first-page":"2313","article-title":"The dantzig selector: Statistical estimation when p is much larger than n","volume":"35","author":"Candes","year":"2007","journal-title":"Ann. Statist."},{"key":"10.1016\/j.patrec.2024.06.011_b11","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/j.1467-9868.2008.00674.x","article-title":"Sure independence screening for ultrahigh dimensional feature space","volume":"70","author":"Fan","year":"2008","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"10.1016\/j.patrec.2024.06.011_b12","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1080\/01621459.2012.695654","article-title":"Feature screening via distance correlation learning","volume":"107","author":"Li","year":"2012","journal-title":"J. Amer. Statist. Assoc."},{"key":"10.1016\/j.patrec.2024.06.011_b13","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1198\/jasa.2011.tm10563","article-title":"Model-free feature screening for ultrahigh-dimensional data","volume":"106","author":"Zhu","year":"2011","journal-title":"J. Amer. Statist. Assoc."},{"year":"2013","series-title":"A Probabilistic Theory of Pattern Recognition","author":"Devroye","key":"10.1016\/j.patrec.2024.06.011_b14"},{"key":"10.1016\/j.patrec.2024.06.011_b15","doi-asserted-by":"crossref","first-page":"2769","DOI":"10.1214\/009053607000000505","article-title":"Measuring and testing dependence by correlation of distances","volume":"35","author":"Sz\u00e9kely","year":"2007","journal-title":"Ann. Statist."},{"key":"10.1016\/j.patrec.2024.06.011_b16","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1080\/10618600.2021.1938585","article-title":"The chi-square test of distance correlation","volume":"31","author":"Shen","year":"2022","journal-title":"J. Comput. Graph. Statist."},{"key":"10.1016\/j.patrec.2024.06.011_b17","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1007\/s10182-020-00378-1","article-title":"The exact equivalence of distance and kernel methods in hypothesis testing","volume":"105","author":"Shen","year":"2021","journal-title":"AStA Adv. Stat. Anal."},{"key":"10.1016\/j.patrec.2024.06.011_b18","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1080\/01621459.2014.993081","article-title":"Conditional distance correlation","volume":"110","author":"Wang","year":"2015","journal-title":"J. Amer. Statist. Assoc."},{"key":"10.1016\/j.patrec.2024.06.011_b19","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1093\/biomet\/asx082","article-title":"Testing independence for multivariate time series via the auto-distance correlation matrix","volume":"105","author":"Fokianos","year":"2018","journal-title":"Biometrika"},{"year":"2024","series-title":"High-dimensional independence testing via maximum and average distance correlations","author":"Shen","key":"10.1016\/j.patrec.2024.06.011_b20"},{"key":"10.1016\/j.patrec.2024.06.011_b21","article-title":"Independence testing for temporal data","author":"Shen","year":"2024","journal-title":"Trans. Mach. Learn. Res."},{"key":"10.1016\/j.patrec.2024.06.011_b22","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.41690","article-title":"Discovering and deciphering relationships across disparate data modalities","volume":"8","author":"Vogelstein","year":"2019","journal-title":"eLife"},{"key":"10.1016\/j.patrec.2024.06.011_b23","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1080\/01621459.2018.1543125","article-title":"From distance correlation to multiscale graph correlation","volume":"115","author":"Shen","year":"2020","journal-title":"J. Amer. Statist. Assoc."},{"key":"10.1016\/j.patrec.2024.06.011_b24","doi-asserted-by":"crossref","first-page":"290","DOI":"10.5486\/PMD.1959.6.3-4.12","article-title":"On random graphs i","volume":"6","author":"Erdos","year":"1959","journal-title":"Publ. Math. Debrecen"},{"key":"10.1016\/j.patrec.2024.06.011_b25","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1093\/biomet\/28.3-4.321","article-title":"Relations between two sets of variates","volume":"28","author":"Hotelling","year":"1936","journal-title":"Biometrika"},{"key":"10.1016\/j.patrec.2024.06.011_b26","doi-asserted-by":"crossref","first-page":"257","DOI":"10.2307\/2347233","article-title":"A unifying tool for linear multivariate statistical methods: the rv-coefficient","author":"Robert","year":"1976","journal-title":"Appl. Stat."},{"key":"10.1016\/j.patrec.2024.06.011_b27","doi-asserted-by":"crossref","first-page":"9868","DOI":"10.1073\/pnas.87.24.9868","article-title":"Brain magnetic resonance imaging with contrast dependent on blood oxygenation","volume":"87","author":"Ogawa","year":"1990","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.patrec.2024.06.011_b28","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2017.17","article-title":"Longitudinal test-retest neuroimaging data from healthy young adults in southwest china","volume":"4","author":"Liu","year":"2017","journal-title":"Sci. Data"},{"key":"10.1016\/j.patrec.2024.06.011_b29","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0144963","article-title":"Individual variability and test-retest reliability revealed by ten repeated resting-state brain scans over one month","volume":"10","author":"Chen","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.patrec.2024.06.011_b30","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","article-title":"An automated labeling system for subdividing the human cerebral cortex on mri scans into gyral based regions of interest","volume":"31","author":"Desikan","year":"2006","journal-title":"Neuroimage"},{"key":"10.1016\/j.patrec.2024.06.011_b31","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1016\/j.neuroimage.2009.01.021","article-title":"Genetic dissection of the mouse brain using high-field magnetic resonance microscopy","volume":"45","author":"Badea","year":"2009","journal-title":"Neuroimage"},{"key":"10.1016\/j.patrec.2024.06.011_b32","doi-asserted-by":"crossref","first-page":"4628","DOI":"10.1093\/cercor\/bhv121","article-title":"A diffusion mri tractography connectome of the mouse brain and comparison with neuronal tracer data","volume":"25","author":"Calabrese","year":"2015","journal-title":"Cerebral Cortex"},{"key":"10.1016\/j.patrec.2024.06.011_b33","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.neuroimage.2010.06.067","article-title":"Waxholm space: an image-based reference for coordinating mouse brain research","volume":"53","author":"Johnson","year":"2010","journal-title":"Neuroimage"},{"key":"10.1016\/j.patrec.2024.06.011_b34","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1016\/j.neuroimage.2010.09.025","article-title":"A reproducible evaluation of ants similarity metric performance in brain image registration","volume":"54","author":"Avants","year":"2011","journal-title":"Neuroimage"},{"key":"10.1016\/j.patrec.2024.06.011_b35","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0080713","article-title":"Deterministic diffusion fiber tracking improved by quantitative anisotropy","volume":"8","author":"Yeh","year":"2013","journal-title":"PLoS One"},{"key":"10.1016\/j.patrec.2024.06.011_b36","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1016\/j.neuroimage.2007.02.023","article-title":"Sexual dimorphism revealed in the structure of the mouse brain using three-dimensional magnetic resonance imaging","volume":"35","author":"Spring","year":"2007","journal-title":"Neuroimage"},{"key":"10.1016\/j.patrec.2024.06.011_b37","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.neuroimage.2013.07.052","article-title":"High resolution whole brain imaging of anatomical variation in xo, xx, and xy mice","volume":"83","author":"Raznahan","year":"2013","journal-title":"Neuroimage"},{"key":"10.1016\/j.patrec.2024.06.011_b38","doi-asserted-by":"crossref","first-page":"2139","DOI":"10.1109\/TNNLS.2022.3187165","article-title":"Learning fair representations via distance correlation minimization","volume":"35","author":"Guo","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patrec.2024.06.011_b39","doi-asserted-by":"crossref","unstructured":"X. Zhen, Z. Meng, R. Chakraborty, V. Singh, On the versatile uses of partial distance correlation in deep learning, in: European Conference on Computer Vision, 2022, pp. 327\u2013346.","DOI":"10.1007\/978-3-031-19809-0_19"},{"key":"10.1016\/j.patrec.2024.06.011_b40","first-page":"3513","article-title":"Seeded graph matching for correlated Erdos-Renyi graphs","volume":"15","author":"Lyzinski","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.patrec.2024.06.011_b41","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1109\/TPAMI.2015.2424894","article-title":"Graph matching: Relax at your own risk","volume":"38","author":"Lyzinski","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Pattern Recognition Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167865524001818?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167865524001818?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T23:28:24Z","timestamp":1750116504000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167865524001818"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":41,"alternative-id":["S0167865524001818"],"URL":"https:\/\/doi.org\/10.1016\/j.patrec.2024.06.011","relation":{},"ISSN":["0167-8655"],"issn-type":[{"type":"print","value":"0167-8655"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Discovering the signal subgraph: An iterative screening approach on graphs","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition Letters","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patrec.2024.06.011","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}