{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T14:13:05Z","timestamp":1725804785404},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319105802"},{"type":"electronic","value":"9783319105819"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"DOI":"10.1007\/978-3-319-10581-9_24","type":"book-chapter","created":{"date-parts":[[2014,9,5]],"date-time":"2014-09-05T10:05:03Z","timestamp":1409911503000},"page":"190-197","source":"Crossref","is-referenced-by-count":2,"title":["Network-Guided Group Feature Selection for Classification of Autism Spectrum Disorder"],"prefix":"10.1007","author":[{"given":"Veronika","family":"Cheplygina","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David M. J.","family":"Tax","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Loog","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aasa","family":"Feragen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"24_CR1","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.brainres.2010.11.076","volume":"1380","author":"K.A. Stigler","year":"2011","unstructured":"Stigler, K.A., et al.: Structural and functional magnetic resonance imaging of autism spectrum disorders. Brain Research\u00a01380, 146\u2013161 (2011)","journal-title":"Brain Research"},{"issue":"3","key":"24_CR2","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1038\/nrn2575","volume":"10","author":"E. Bullmore","year":"2009","unstructured":"Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci.\u00a010(3), 186\u2013198 (2009)","journal-title":"Nat. Rev. Neurosci."},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Rudie, J., Brown, J., et al.: Altered functional and structural brain network organization in autism. NeuroImage: Clinical (2012)","DOI":"10.1016\/j.nicl.2012.11.006"},{"key":"24_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/978-3-642-40811-3_12","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"Y. Ghanbari","year":"2013","unstructured":"Ghanbari, Y., Smith, A.R., Schultz, R.T., Verma, R.: Connectivity subnetwork learning for pathology and developmental variations. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol.\u00a08149, pp. 90\u201397. Springer, Heidelberg (2013)"},{"issue":"12","key":"24_CR5","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1016\/j.tics.2013.10.007","volume":"17","author":"M. Rubinov","year":"2013","unstructured":"Rubinov, M., Bullmore, E.: Fledgling pathoconnectomics of psychiatric disorders. Trends in Cognitive Sciences\u00a017(12), 641\u2013647 (2013)","journal-title":"Trends in Cognitive Sciences"},{"issue":"1","key":"24_CR6","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.neuroimage.2009.08.024","volume":"49","author":"C. Ecker","year":"2010","unstructured":"Ecker, C., et al.: Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage\u00a049(1), 44\u201356 (2010)","journal-title":"Neuroimage"},{"key":"24_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1007\/978-3-642-15705-9_68","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2010","author":"M. Ingalhalikar","year":"2010","unstructured":"Ingalhalikar, M., Kanterakis, S., Gur, R., Roberts, T.P.L., Verma, R.: DTI based diagnostic prediction of a disease via pattern classification. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol.\u00a06361, pp. 558\u2013565. Springer, Heidelberg (2010)"},{"key":"24_CR8","unstructured":"Ghiassian, S., et al.: Learning to Classify Psychiatric Disorders based on fMR Images: Autism vs Healthy and ADHD vs Healthy. In: MLINI (2013)"},{"issue":"3","key":"24_CR9","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1137\/110832380","volume":"5","author":"R. Jenatton","year":"2012","unstructured":"Jenatton, R., et al.: Multiscale mining of fMRI data with hierarchical structured sparsity. SIAM J. on Imaging Sciences\u00a05(3), 835\u2013856 (2012)","journal-title":"SIAM J. on Imaging Sciences"},{"issue":"4","key":"24_CR10","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1016\/j.neubiorev.2012.01.004","volume":"36","author":"G. Orr\u00f9","year":"2012","unstructured":"Orr\u00f9, G., et al.: Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosc. Biobeh. Rev.\u00a036(4), 1140\u20131152 (2012)","journal-title":"Neurosc. Biobeh. Rev."},{"issue":"4","key":"24_CR11","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1214\/12-STS394","volume":"27","author":"F. Bach","year":"2012","unstructured":"Bach, F., Jenatton, R., Mairal, J., Obozinski, G.: Structured sparsity through convex optimization. Statistical Science\u00a027(4), 450\u2013468 (2012)","journal-title":"Statistical Science"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Brown, J.A., et al.: The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis. Frontiers in Neuroinformatics\u00a06 (2012)","DOI":"10.3389\/fninf.2012.00028"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Skurichina, M., Duin, R.P.W.: Stabilizing classifiers for very small sample sizes. In: International Conference on Pattern Recognition, vol.\u00a02, pp. 891\u2013896. IEEE (1996)","DOI":"10.1109\/ICPR.1996.547204"},{"issue":"4","key":"24_CR14","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1016\/j.neuron.2011.09.006","volume":"72","author":"J.D. Power","year":"2011","unstructured":"Power, J.D., et al.: Functional network organization of the human brain. Neuron\u00a072(4), 665\u2013678 (2011)","journal-title":"Neuron"},{"issue":"8","key":"24_CR15","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H. Peng","year":"2005","unstructured":"Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE TPAMI\u00a027(8), 1226\u20131238 (2005)","journal-title":"IEEE TPAMI"},{"key":"24_CR16","first-page":"27","volume":"13","author":"G. Brown","year":"2012","unstructured":"Brown, G., et al.: Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. JMLR\u00a013, 27\u201366 (2012)","journal-title":"JMLR"},{"issue":"1-3","key":"24_CR17","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I. Guyon","year":"2002","unstructured":"Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning\u00a046(1-3), 389\u2013422 (2002)","journal-title":"Machine Learning"},{"key":"24_CR18","unstructured":"Duin, R.P.W., et al.: PRTools, a MATLAB toolbox for pattern recognition (2010), \n                  \n                    http:\/\/www.prtools.org"},{"key":"24_CR19","unstructured":"Liu, J., Ji, S., Ye, J.: SLEP: Sparse Learning with Efficient Projections (2009)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Azencott, C.A., et al.: Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics\u00a029(13), i171\u2013i179 (2013)","DOI":"10.1093\/bioinformatics\/btt238"},{"issue":"2","key":"24_CR21","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.cortex.2011.05.018","volume":"48","author":"M. Langen","year":"2012","unstructured":"Langen, M., et al.: Fronto-striatal circuitry and inhibitory control in autism: findings from diffusion tensor imaging tractography. Cortex\u00a048(2), 183\u2013193 (2012)","journal-title":"Cortex"},{"issue":"1","key":"24_CR22","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1016\/j.neubiorev.2011.09.003","volume":"36","author":"M.E. Vissers","year":"2012","unstructured":"Vissers, M.E., et al.: Brain connectivity and high functioning autism: a promising path of research that needs refined models, methodological convergence, and stronger behavioral links. Neurosci. Biobehav. Rev.\u00a036(1), 604\u2013625 (2012)","journal-title":"Neurosci. Biobehav. Rev."},{"issue":"5","key":"24_CR23","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1002\/aur.1243","volume":"5","author":"B.G. Travers","year":"2012","unstructured":"Travers, B.G., et al.: Diffusion tensor imaging in autism spectrum disorder: a review. Autism Research\u00a05(5), 289\u2013313 (2012)","journal-title":"Autism Research"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-10581-9_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,27]],"date-time":"2019-05-27T16:20:14Z","timestamp":1558974014000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-10581-9_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"ISBN":["9783319105802","9783319105819"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-10581-9_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2014]]}}}