{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T14:18:06Z","timestamp":1726409886717},"publisher-location":"Berlin, Heidelberg","reference-count":23,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540776215"},{"type":"electronic","value":"9783540776239"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008]]},"DOI":"10.1007\/978-3-540-77623-9_14","type":"book-chapter","created":{"date-parts":[[2008,9,3]],"date-time":"2008-09-03T01:04:33Z","timestamp":1220403873000},"page":"237-258","source":"Crossref","is-referenced-by-count":0,"title":["Support Vector Machines and Neural Networks as Marker Selectors in Cancer Gene Analysis"],"prefix":"10.1007","author":[{"given":"Michalis E.","family":"Blazadonakis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michalis","family":"Zervakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"14_CR1_1","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1038\/35000501","volume":"403","author":"A Alizadeh","year":"2000","unstructured":"Alizadeh A, Eisen M, Davis RE, et al. (2000) Distinct substypes of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 403:503\u2013511","journal-title":"Nature"},{"key":"14_CR2_1","doi-asserted-by":"publisher","first-page":"6562","DOI":"10.1073\/pnas.102102699","volume":"99","author":"C Ambroise","year":"2002","unstructured":"Ambroise C, McLachlan G (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. PNAS, 99:6562\u20136566","journal-title":"PNAS"},{"key":"14_CR3_1","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/ng765","volume":"30","author":"S Armstrong","year":"2002","unstructured":"Armstrong S, Staunton J, Silverman L, et al. (2002) MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nature Genetics, 30:41\u201347","journal-title":"Nature Genetics"},{"key":"14_CR4_1","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1093\/bioinformatics\/18.2.319","volume":"18","author":"F Azuaje","year":"2002","unstructured":"Azuaje F (2002) A cluster validity frame work for genome expression data. Bionformatics, 18:319\u2013320","journal-title":"Bionformatics"},{"key":"14_CR5_1","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/5326.923275","volume":"31","author":"S Bandyopadhyay","year":"2001","unstructured":"Bandyopadhyay S, Maulik U (2001) Nonparametric genetic clustering of validity indices. IEEE Transactions on Systems, Man, and Cybernetics, 31:120\u2013126","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"key":"14_CR6_1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex Optimization","author":"S Boyd","year":"2004","unstructured":"Boyd S, Vandenberghe L (2004) Convex Optimization. Oxford University Press, Oxford"},{"key":"14_CR7_1","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","volume":"1","author":"D Davie","year":"1979","unstructured":"Davie D, Bouldin, DW. (1979) A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, 1:224\u2013227","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI"},{"key":"14_CR8_1","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub TR, Slonim DK, Tamayo P, et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286:531\u2013536","journal-title":"Science"},{"key":"14_CR9_1","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"36","author":"I Guyon","year":"2002","unstructured":"Guyon I, Weston J, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Machine Learning, 36:389\u2013422","journal-title":"Machine Learning"},{"issue":"2","key":"14_CR10_1","first-page":"1","volume":"1","author":"T Hestie","year":"2000","unstructured":"Hestie T, Tibshirani R, Eisen MB, et al. (2000) Gene shaving as a method for identifying distinct set of genes with similar expression patterns. Journal of Genome Biology, 1(2):1\u201321","journal-title":"Journal of Genome Biology"},{"key":"14_CR11_1","doi-asserted-by":"publisher","first-page":"3741","DOI":"10.1093\/bioinformatics\/bti618","volume":"21","author":"F Li","year":"2005","unstructured":"Li F, Yang Y (2005) Analysis of recursive gene selection approaches from microarray data. Bioinformatics, 21, 3741\u20133747","journal-title":"Bioinformatics"},{"key":"14_CR12_1","series-title":"Wiley Series in Probability and Mathematical Statistics","volume-title":"Statistical Analysis with Missing Data","author":"A Little","year":"1987","unstructured":"Little A, Rubin D (1987) Statistical Analysis with Missing Data. Wiley Series in Probability and Mathematical Statistics. Wiley, New York"},{"key":"14_CR13_1","first-page":"1602","volume":"63","author":"C Nutt","year":"2003","unstructured":"Nutt C, Mani D, Betensky R, et al. (2003) Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Research, 63:1602\u20131607","journal-title":"Cancer Research"},{"key":"14_CR14_1","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1038\/ng1060","volume":"33","author":"S Ramaswamy","year":"2003","unstructured":"Ramaswamy S, Ross K, Lander E, et al. (2003) A molecular signature of metastasis in primary solid tumors. Nature Genetics, 33:49\u201354","journal-title":"Nature Genetics"},{"key":"14_CR15_1","doi-asserted-by":"crossref","unstructured":"Riedmiller M, Braun H (1993) A direct adoptive method for faster backpropagation learning: The RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN), 586\u2013591","DOI":"10.1109\/ICNN.1993.298623"},{"key":"14_CR16_1","doi-asserted-by":"publisher","first-page":"2635","DOI":"10.1093\/bioinformatics\/btl442","volume":"22","author":"R Shen","year":"2006","unstructured":"Shen R, Ghosh D, Chinnaiyan A, et al. (2006) Eigengene-based linear discriminant model for tumor classification using gene expression microarray data. Bioinformatics, 22:2635\u20132642","journal-title":"Bioinformatics"},{"key":"14_CR17_1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1093\/jnci\/95.1.4","volume":"95","author":"R Simon","year":"2003","unstructured":"Simon R, Radmacher M, Dobbin K, et al. (2003) Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of the National Cancer Institute, 95:4\u201318","journal-title":"Journal of the National Cancer Institute"},{"key":"14_CR18_1","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.1056\/NEJMoa021967","volume":"347","author":"MJ Vijver Van De","year":"2002","unstructured":"Van De Vijver MJ, He YD, Van\u2019t Veer LJ, et al. (2002) A gene expression signature as a predictor of survival in breast cancer. The New England Journal of Medicine, 347:1999\u20132009","journal-title":"The New England Journal of Medicine"},{"key":"14_CR19_1","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1038\/415530a","volume":"415","author":"LJ Van\u2019t Veer","year":"2002","unstructured":"Van\u2019t Veer LJ, Dai H, Van de Vijver, et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Letters to Nature, 415:530\u2013536","journal-title":"Letters to Nature"},{"key":"14_CR20_1","volume-title":"The Nature of Statistical Learning Theory","author":"NV Vapnik","year":"1999","unstructured":"Vapnik NV (1999) The Nature of Statistical Learning Theory. Springer, Berlin Heidelberg New York"},{"key":"14_CR21_1","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1109\/72.846731","volume":"11","author":"J Vesanto","year":"2000","unstructured":"Vesanto J, Alhoniemi E (2000) Clustering of the self organizing map. IEEE Transactions on Neural Networks, 11:586\u2013600","journal-title":"IEEE Transactions on Neural Networks"},{"key":"14_CR22_1","doi-asserted-by":"crossref","unstructured":"Wang J, Delabie J, Aashein H, Smeland E, Myklebost O (2002) Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study. BMC Bioinformatics, 3:\n                    http:\/\/www.biomedcentral.com\/1471-2105\/3\/36","DOI":"10.1186\/1471-2105-3-36"},{"issue":"8","key":"14_CR23_1","doi-asserted-by":"publisher","first-page":"1530","DOI":"10.1093\/bioinformatics\/bti192","volume":"21","author":"Y Wang","year":"2004","unstructured":"Wang Y, Makedon F, Ford J, et al. (2004) HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data. Bioinformatics, 21(8):1530\u20131537","journal-title":"Bioinformatics"}],"container-title":["Studies in Computational Intelligence","Intelligent Techniques and Tools for Novel System Architectures"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-540-77623-9_14.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T10:47:12Z","timestamp":1619520432000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-540-77623-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008]]},"ISBN":["9783540776215","9783540776239"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-540-77623-9_14","relation":{},"ISSN":["1860-949X"],"issn-type":[{"type":"print","value":"1860-949X"}],"subject":[],"published":{"date-parts":[[2008]]}}}