{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:12:31Z","timestamp":1779127951049,"version":"3.51.4"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T00:00:00Z","timestamp":1654128000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T00:00:00Z","timestamp":1654128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["R01MH120299"],"award-info":[{"award-number":["R01MH120299"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s10994-022-06174-z","type":"journal-article","created":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T23:03:41Z","timestamp":1654211021000},"page":"3733-3767","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Semi-parametric Bayes regression with network-valued covariates"],"prefix":"10.1007","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6631-9223","authenticated-orcid":false,"given":"Xin","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-4875","authenticated-orcid":false,"given":"Suprateek","family":"Kundu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4674-0314","authenticated-orcid":false,"given":"Jennifer","family":"Stevens","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,2]]},"reference":[{"issue":"3","key":"6174_CR1","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1007\/s10618-014-0365-y","volume":"29","author":"L Akoglu","year":"2015","unstructured":"Akoglu, L., Tong, H., & Koutra, D. (2015). Graph based anomaly detection and description: a survey. Data Mining and Knowledge Discovery, 29(3), 626\u2013688.","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"6","key":"6174_CR2","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1177\/1073858406293182","volume":"12","author":"DS Bassett","year":"2006","unstructured":"Bassett, D. S., & Bullmore, E. (2006). Small-world brain networks. The Neuroscientist, 12(6), 512\u2013523.","journal-title":"The Neuroscientist"},{"issue":"6","key":"6174_CR3","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1162\/089976603321780317","volume":"15","author":"M Belkin","year":"2003","unstructured":"Belkin, M., & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6), 1373\u20131396.","journal-title":"Neural Computation"},{"issue":"3","key":"6174_CR4","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1038\/nrn2575","volume":"10","author":"E Bullmore","year":"2009","unstructured":"Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186.","journal-title":"Nature Reviews Neuroscience"},{"issue":"2","key":"6174_CR5","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1093\/biomet\/asq017","volume":"97","author":"CM Carvalho","year":"2010","unstructured":"Carvalho, C. M., Polson, N. G., & Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika, 97(2), 465\u2013480.","journal-title":"Biometrika"},{"issue":"4","key":"6174_CR6","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1111\/biom.12882","volume":"74","author":"C Chang","year":"2018","unstructured":"Chang, C., Kundu, S., & Long, Q. (2018). Scalable bayesian variable selection for structured high-dimensional data. Biometrics, 74(4), 1372\u20131382.","journal-title":"Biometrics"},{"issue":"368","key":"6174_CR7","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/01621459.1979.10481038","volume":"74","author":"WS Cleveland","year":"1979","unstructured":"Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829\u2013836.","journal-title":"Journal of the American Statistical Association"},{"issue":"9","key":"6174_CR8","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1038\/nn.3470","volume":"16","author":"MW Cole","year":"2013","unstructured":"Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16(9), 1348.","journal-title":"Nature Neuroscience"},{"issue":"2","key":"6174_CR9","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1002\/da.10113","volume":"18","author":"KM Connor","year":"2003","unstructured":"Connor, K. M., & Davidson, J. R. (2003). Development of a new resilience scale: The connor-davidson resilience scale (cd-risc). Depression and Anxiety, 18(2), 76\u201382.","journal-title":"Depression and Anxiety"},{"issue":"6","key":"6174_CR10","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1002\/mrm.22159","volume":"62","author":"RC Craddock","year":"2009","unstructured":"Craddock, R. C., Holtzheimer, P. E., III., Hu, X. P., & Mayberg, H. S. (2009). Disease state prediction from resting state functional connectivity. Magnetic Resonance in Medicine: An Official Journal of the Int Soc for Magnetic Resonance in Medicine, 62(6), 1619\u20131628.","journal-title":"Magnetic Resonance in Medicine: An Official Journal of the Int Soc for Magnetic Resonance in Medicine"},{"issue":"5","key":"6174_CR11","first-page":"1","volume":"1695","author":"G Csardi","year":"2006","unstructured":"Csardi, G., Nepusz, T., et al. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1\u20139.","journal-title":"InterJournal, Complex Systems"},{"issue":"5","key":"6174_CR12","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1109\/TKDE.2018.2849727","volume":"31","author":"P Cui","year":"2018","unstructured":"Cui, P., Wang, X., Pei, J., & Zhu, W. (2018). A survey on network embedding. IEEE Transactions on Knowledge and Data Engineering, 31(5), 833\u2013852.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"11","key":"6174_CR13","doi-asserted-by":"crossref","first-page":"4883","DOI":"10.1109\/TITS.2019.2950416","volume":"21","author":"Z Cui","year":"2019","unstructured":"Cui, Z., Henrickson, K., Ke, R., & Wang, Y. (2019). Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. IEEE Transactions on Intelligent Transportation Systems, 21(11), 4883\u20134894.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"6174_CR14","doi-asserted-by":"crossref","first-page":"525","DOI":"10.3389\/fnins.2018.00525","volume":"12","author":"Y Du","year":"2018","unstructured":"Du, Y., Fu, Z., & Calhoun, V. D. (2018). Classification and prediction of brain disorders using functional connectivity: Promising but challenging. Frontiers in Neuroscience, 12, 525.","journal-title":"Frontiers in Neuroscience"},{"issue":"520","key":"6174_CR15","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1080\/01621459.2016.1219260","volume":"112","author":"D Durante","year":"2017","unstructured":"Durante, D., Dunson, D. B., & Vogelstein, J. T. (2017). Nonparametric bayes modeling of populations of networks. Journal of the American Statistical Association, 112(520), 1516\u20131530.","journal-title":"Journal of the American Statistical Association"},{"issue":"5","key":"6174_CR16","first-page":"413","volume":"33","author":"E Falconer","year":"2008","unstructured":"Falconer, E., Bryant, R., Felmingham, K. L., Kemp, A. H., Gordon, E., Peduto, A., et al. (2008). The neural networks of inhibitory control in posttraumatic stress disorder. Journal of Psychiatry & Neuroscience: JPN, 33(5), 413.","journal-title":"Journal of Psychiatry & Neuroscience: JPN"},{"issue":"3","key":"6174_CR17","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1093\/biostatistics\/kxm045","volume":"9","author":"J Friedman","year":"2008","unstructured":"Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432\u2013441.","journal-title":"Biostatistics"},{"issue":"1","key":"6174_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1.","journal-title":"Journal of Statistical Software"},{"key":"6174_CR19","doi-asserted-by":"crossref","unstructured":"Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In: IN BAYESIAN STATISTICS, Citeseer","DOI":"10.21034\/sr.148"},{"key":"6174_CR20","unstructured":"Gramacy, R.B. (2018). monomvn: Estimation for Multivariate Normal and Student-t Data with Monotone Missingness. https:\/\/CRAN.R-project.org\/package=monomvn, r package version 1.9-8"},{"issue":"534","key":"6174_CR21","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1080\/01621459.2020.1772079","volume":"16","author":"S Guha","year":"2020","unstructured":"Guha, S., & Rodriguez, A. (2020). Bayesian regression with undirected network predictors with an application to brain connectome data. Journal of the American Statistical Association, 16(534), 581\u2013593.","journal-title":"Journal of the American Statistical Association"},{"issue":"1","key":"6174_CR22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/netn_a_00054","volume":"3","author":"MN Hallquist","year":"2018","unstructured":"Hallquist, M. N., & Hillary, F. G. (2018). Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world. Network Neuroscience, 3(1), 1\u201326.","journal-title":"Network Neuroscience"},{"issue":"15","key":"6174_CR23","doi-asserted-by":"crossref","first-page":"4518","DOI":"10.1002\/hbm.24718","volume":"40","author":"IA Higgins","year":"2019","unstructured":"Higgins, I. A., Kundu, S., Choi, K. S., Mayberg, H. S., & Guo, Y. (2019). A difference degree test for comparing brain networks. Human Brain Mapping, 40(15), 4518\u20134536.","journal-title":"Human Brain Mapping"},{"issue":"1","key":"6174_CR24","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","volume":"12","author":"AE Hoerl","year":"1970","unstructured":"Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55\u201367.","journal-title":"Technometrics"},{"issue":"469","key":"6174_CR25","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1198\/016214504000001015","volume":"100","author":"PD Hoff","year":"2005","unstructured":"Hoff, P. D. (2005). Bilinear mixed-effects models for dyadic data. Journal of the American Statistical Association, 100(469), 286\u2013295.","journal-title":"Journal of the American Statistical Association"},{"issue":"460","key":"6174_CR26","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1198\/016214502388618906","volume":"97","author":"PD Hoff","year":"2002","unstructured":"Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090\u20131098.","journal-title":"Journal of the American Statistical Association"},{"issue":"4","key":"6174_CR27","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1214\/009053607000000019","volume":"35","author":"W Jiang","year":"2007","unstructured":"Jiang, W., et al. (2007). Bayesian variable selection for high dimensional generalized linear models: Convergence rates of the fitted densities. The Annals of Statistics, 35(4), 1487\u20131511.","journal-title":"The Annals of Statistics"},{"key":"6174_CR28","doi-asserted-by":"crossref","unstructured":"Kraemer, G., Reichstein, M., & D, M.M. (2018). dimRed and coRanking\u2014unifying dimensionality reduction in r. The R Journal 10(1), 342\u2013358, https:\/\/journal.r-project.org\/archive\/2018\/RJ-2018-039\/index.html, coRanking version 0.2.2","DOI":"10.32614\/RJ-2018-039"},{"issue":"3","key":"6174_CR29","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1093\/biomet\/asu019","volume":"101","author":"S Kundu","year":"2014","unstructured":"Kundu, S., & Dunson, D. B. (2014). Latent factor models for density estimation. Biometrika, 101(3), 641\u2013654.","journal-title":"Biometrika"},{"issue":"2","key":"6174_CR30","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1214\/17-BA1086","volume":"14","author":"S Kundu","year":"2019","unstructured":"Kundu, S., Mallick, B. K., Baladandayuthapani, V., et al. (2019). Efficient bayesian regularization for graphical model selection. Bayesian Analysis, 14(2), 449\u2013476.","journal-title":"Bayesian Analysis"},{"issue":"8","key":"6174_CR31","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.jpsychires.2005.07.007","volume":"40","author":"R Lanius","year":"2006","unstructured":"Lanius, R., Bluhm, R., Lanius, U., & Pain, C. (2006). A review of neuroimaging studies in ptsd: Heterogeneity of response to symptom provocation. Journal of Psychiatric Research, 40(8), 709\u2013729.","journal-title":"Journal of Psychiatric Research"},{"key":"6174_CR32","unstructured":"Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926"},{"issue":"7","key":"6174_CR33","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1002\/asi.20591","volume":"58","author":"D Liben-Nowell","year":"2007","unstructured":"Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the American society for Information Science and Technology, 58(7), 1019\u20131031.","journal-title":"Journal of the American society for Information Science and Technology"},{"issue":"534","key":"6174_CR34","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1080\/01621459.2020.1796357","volume":"116","author":"J Lukemire","year":"2020","unstructured":"Lukemire, J., Kundu, S., Pagnoni, G., & Guo, Y. (2020). Bayesian joint modeling of multiple brain functional networks. Journal of the American Statistical Association, 116(534), 518\u2013530.","journal-title":"Journal of the American Statistical Association"},{"key":"6174_CR35","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3389\/fncom.2018.00095","volume":"12","author":"L Meng","year":"2018","unstructured":"Meng, L., & Xiang, J. (2018). Brain network analysis and classification based on convolutional neural network. Frontiers in Computational Neuroscience, 12, 95.","journal-title":"Frontiers in Computational Neuroscience"},{"key":"6174_CR36","doi-asserted-by":"crossref","unstructured":"Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., & Bronstein, M.M. (2017). Geometric deep learning on graphs and manifolds using mixture model cnns. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5115\u20135124","DOI":"10.1109\/CVPR.2017.576"},{"issue":"455","key":"6174_CR37","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1198\/016214501753208735","volume":"96","author":"K Nowicki","year":"2001","unstructured":"Nowicki, K., & Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96(455), 1077\u20131087.","journal-title":"Journal of the American Statistical Association"},{"issue":"501","key":"6174_CR38","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1080\/01621459.2013.763566","volume":"108","author":"G Page","year":"2013","unstructured":"Page, G., Bhattacharya, A., & Dunson, D. (2013). Classification via bayesian nonparametric learning of affine subspaces. Journal of the American Statistical Association, 108(501), 187\u2013201.","journal-title":"Journal of the American Statistical Association"},{"issue":"504","key":"6174_CR39","doi-asserted-by":"crossref","first-page":"1339","DOI":"10.1080\/01621459.2013.829001","volume":"108","author":"NG Polson","year":"2013","unstructured":"Polson, N. G., Scott, J. G., & Windle, J. (2013). Bayesian inference for logistic models using p\u00f3lya-gamma latent variables. Journal of the American Statistical Association, 108(504), 1339\u20131349.","journal-title":"Journal of the American Statistical Association"},{"issue":"4","key":"6174_CR40","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/j.neuron.2011.09.006","volume":"72","author":"JD Power","year":"2011","unstructured":"Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., Vogel, A. C., Laumann, T. O., Miezin, F. M., Schlaggar, B. L., et al. (2011). Functional network organization of the human brain. Neuron, 72(4), 665\u2013678.","journal-title":"Neuron"},{"key":"6174_CR41","volume-title":"Gaussian processes for machine learning","author":"CE Rasmussen","year":"2006","unstructured":"Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. MA: MIT Press Cambridge."},{"issue":"3","key":"6174_CR42","first-page":"1648","volume":"13","author":"JDA Reli\u00f3n","year":"2019","unstructured":"Reli\u00f3n, J. D. A., Kessler, D., Levina, E., & Taylor, S. F. (2019). Network classification with applications to brain connectomics. The Annals of Applied Statistics, 13(3), 1648.","journal-title":"The Annals of Applied Statistics"},{"key":"6174_CR43","unstructured":"Robert, C.P. (2015). The metropolis-hastings algorithm. arXiv preprint arXiv:1504.01896"},{"issue":"1","key":"6174_CR44","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1214\/11-STS354","volume":"26","author":"T Savitsky","year":"2011","unstructured":"Savitsky, T., Vannucci, M., & Sha, N. (2011). Variable selection for nonparametric gaussian process priors: Models and computational strategies. Statistical Science: A Review Journal of the Institute of Mathematical Statistics, 26(1), 130.","journal-title":"Statistical Science: A Review Journal of the Institute of Mathematical Statistics"},{"issue":"4","key":"6174_CR45","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1023\/A:1013058625719","volume":"14","author":"CD Scher","year":"2001","unstructured":"Scher, C. D., Stein, M. B., Asmundson, G. J., McCreary, D. R., & Forde, D. R. (2001). The childhood trauma questionnaire in a community sample: Psychometric properties and normative data. Journal of Traumatic Stress, 14(4), 843\u2013857.","journal-title":"Journal of Traumatic Stress"},{"issue":"3","key":"6174_CR46","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/BF02229182","volume":"19","author":"G Sprang","year":"1997","unstructured":"Sprang, G. (1997). The traumatic experiences inventory (tei): A test of psychometric properties. Journal of Psychopathology and Behavioral Assessment, 19(3), 257\u2013271.","journal-title":"Journal of Psychopathology and Behavioral Assessment"},{"key":"6174_CR47","doi-asserted-by":"crossref","first-page":"672","DOI":"10.3389\/fnhum.2013.00672","volume":"7","author":"RK Sripada","year":"2013","unstructured":"Sripada, R. K., Garfinkel, S. N., & Liberzon, I. (2013). Avoidant symptoms in ptsd predict fear circuit activation during multimodal fear extinction. Frontiers in Human Neuroscience, 7, 672.","journal-title":"Frontiers in Human Neuroscience"},{"issue":"10","key":"6174_CR48","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1016\/j.jpsychires.2013.05.031","volume":"47","author":"JS Stevens","year":"2013","unstructured":"Stevens, J. S., Jovanovic, T., Fani, N., Ely, T. D., Glover, E. M., Bradley, B., & Ressler, K. J. (2013). Disrupted amygdala-prefrontal functional connectivity in civilian women with posttraumatic stress disorder. Journal of Psychiatric Research, 47(10), 1469\u20131478.","journal-title":"Journal of Psychiatric Research"},{"issue":"5500","key":"6174_CR49","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","volume":"290","author":"JB Tenenbaum","year":"2000","unstructured":"Tenenbaum, J. B., De Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319\u20132323.","journal-title":"Science"},{"issue":"1","key":"6174_CR50","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267\u2013288.","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"key":"6174_CR51","doi-asserted-by":"crossref","unstructured":"Weaver, C., Xiao, L., & Lindquist, M.A. (2021). Single-index models with functional connectivity network predictors. Biostatistics https:\/\/doi.org\/10.1093\/biostatistics\/kxab015","DOI":"10.1093\/biostatistics\/kxab015"},{"issue":"1","key":"6174_CR52","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Philip, S. Y. (2020). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4\u201324.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"2","key":"6174_CR53","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1214\/15-AOS1390","volume":"44","author":"Y Yang","year":"2016","unstructured":"Yang, Y., Dunson, D. B., et al. (2016). Bayesian manifold regression. The Annals of Statistics, 44(2), 876\u2013905.","journal-title":"The Annals of Statistics"},{"key":"6174_CR54","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/542467","author":"X Zhan","year":"2015","unstructured":"Zhan, X., & Yu, R. (2015). A window into the brain: advances in psychiatric fMRI. BioMed Research International. https:\/\/doi.org\/10.1155\/2015\/542467.","journal-title":"BioMed Research International"},{"key":"6174_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhou, D., Yildirim, M.Y., Alcorn, S., He, J., Davulcu, H., & Tong, H. (2017). Hidden: hierarchical dense subgraph detection with application to financial fraud detection. In: Proceedings of the 2017 SIAM International Conference on Data Mining, SIAM, pp 570\u2013578","DOI":"10.1137\/1.9781611974973.64"},{"key":"6174_CR56","doi-asserted-by":"crossref","unstructured":"Zhou, D., Zhang, S., Yildirim, M.Y., Alcorn, S., Tong, H., Davulcu, H., & He, J. (2017). A local algorithm for structure-preserving graph cut. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 655\u2013664","DOI":"10.1145\/3097983.3098015"},{"issue":"2","key":"6174_CR57","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301\u2013320.","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06174-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-022-06174-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06174-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T10:35:29Z","timestamp":1727346929000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-022-06174-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,2]]},"references-count":57,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["6174"],"URL":"https:\/\/doi.org\/10.1007\/s10994-022-06174-z","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,2]]},"assertion":[{"value":"1 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Study procedures were approved by the institutional review board of Emory University, and procedures were consistent with the Declaration of Helsinki. The study protocol involved automatic consent from participants regarding the use of the study data in a deidentified form for secondary analysis. Use of experimental animals, and human participants - The article does not report experiments on live vertebrates and\/or higher invertebrates, and only involves secondary analysis of deidentified data from ADNI.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}