{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:29:48Z","timestamp":1742912988424,"version":"3.40.3"},"publisher-location":"Berlin, Heidelberg","reference-count":18,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642042768"},{"type":"electronic","value":"9783642042775"}],"license":[{"start":{"date-parts":[[2009,1,1]],"date-time":"2009-01-01T00:00:00Z","timestamp":1230768000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009]]},"DOI":"10.1007\/978-3-642-04277-5_20","type":"book-chapter","created":{"date-parts":[[2009,10,1]],"date-time":"2009-10-01T08:49:52Z","timestamp":1254386992000},"page":"195-204","source":"Crossref","is-referenced-by-count":0,"title":["Kernel Alignment k-NN for Human Cancer Classification Using the Gene Expression Profiles"],"prefix":"10.1007","author":[{"given":"Manuel","family":"Mart\u00edn-Merino","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javier","family":"de las Rivas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"20_CR1","first-page":"1","volume":"1","author":"N. Cristianini","year":"2002","unstructured":"Cristianini, N., Kandola, J., Elisseeff, J., Shawe-Taylor, A.: On the kernel target alignment. Journal of Machine Learning Research\u00a01, 1\u201331 (2002)","journal-title":"Journal of Machine Learning Research"},{"issue":"457","key":"20_CR2","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1198\/016214502753479248","volume":"97","author":"S. Dudoit","year":"2002","unstructured":"Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Journal of the American Statistical Association\u00a097(457), 77\u201387 (2002)","journal-title":"Journal of the American Statistical Association"},{"key":"20_CR3","volume-title":"Bioinformatics and Computational Biology Solutions Using R and Bioconductor","author":"R. Gentleman","year":"2006","unstructured":"Gentleman, R., Carey, V., Huber, W., Irizarry, R., Dudoit, S.: Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer, Berlin (2006)"},{"issue":"11","key":"20_CR4","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1109\/TKDE.2004.68","volume":"16","author":"D. Jiang","year":"2004","unstructured":"Jiang, D., Tang, C., Zhang, A.: Cluster Analysis for Gene Expression Data: A Survey. IEEE Transactions on Knowledge and Data Engineering\u00a016(11), 1370\u20131386 (2004)","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"20_CR5","unstructured":"Kandola, J., Shawe-Taylor, J., Cristianini, N.: Optimizing kernel alignment over combinations of kernels., NeuroCOLT, Tech. Rep (2002)"},{"key":"20_CR6","first-page":"27","volume":"3","author":"G. Lanckriet","year":"2004","unstructured":"Lanckriet, G., Cristianini, N., Barlett, P., El Ghaoui, L., Jordan, M.: Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research\u00a03, 27\u201372 (2004)","journal-title":"Journal of Machine Learning Research"},{"key":"20_CR7","first-page":"175","volume":"2","author":"E. Pekalska","year":"2001","unstructured":"Pekalska, E., Paclick, P., Duin, R.: A generalized kernel approach to dissimilarity-based classification. Journal of Machine Learning Research\u00a02, 175\u2013211 (2001)","journal-title":"Journal of Machine Learning Research"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Pomeroy, S.E.A.: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature\u00a0415 (2002)","DOI":"10.1038\/415436a"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Savage, K., et al.: The molecular signature of mediastinal large B-cell lymphoma differs from that of other diffuse large B-cell lymphomas and shares features with classical hodgkin lymphoma. Blood\u00a0102(12) (December 2003)","DOI":"10.1182\/blood-2003-06-1841"},{"key":"20_CR10","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4057.001.0001","volume-title":"Kernel Methods in Computational Biology","author":"B. Scholkopf","year":"2004","unstructured":"Scholkopf, B., Tsuda, K., Vert, J.: Kernel Methods in Computational Biology. MIT Press, Cambridge (2004)"},{"key":"20_CR11","first-page":"1043","volume":"6","author":"C. Soon Ong","year":"2005","unstructured":"Soon Ong, C., Smola, A., Williamson, R.: Learning the kernel with hyperkernels. Journal of Machine Learning Research\u00a06, 1043\u20131071 (2005)","journal-title":"Journal of Machine Learning Research"},{"issue":"5","key":"20_CR12","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1093\/bioinformatics\/bti033","volume":"21","author":"A. Statnikov","year":"2004","unstructured":"Statnikov, A.: A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics\u00a021(5), 631\u2013643 (2004)","journal-title":"Bioinformatics"},{"key":"20_CR13","unstructured":"Tsuda, K.: Support Vector Classifier with Assymetric Kernel Function. In: Proceedings of ESANN, Bruges, pp. 183\u2013188 (1999)"},{"key":"20_CR14","volume-title":"Statistical Learning Theory","author":"V. Vapnik","year":"1998","unstructured":"Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, New York (1998)"},{"key":"20_CR15","first-page":"207","volume":"10","author":"K.Q. Weinberger","year":"2009","unstructured":"Weinberger, K.Q., Saul, L.K.: Distance Metric Learning for Large Margin Nearest Neighbor Classification. J. Machine Learning Research\u00a010, 207\u2013244 (2009)","journal-title":"J. Machine Learning Research"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"West, M., et al.: Predicting the clinical status of human breast cancer by using gene expression profiles. PNAS\u00a098(20) (2001)","DOI":"10.1073\/pnas.201162998"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Wu, G., Chang, E.Y., Panda, N.: Formulating distance functions via the kernel trick. In: ACM SIGKDD, Chicago, pp. 703\u2013709 (2005)","DOI":"10.1145\/1081870.1081960"},{"issue":"299","key":"20_CR18","first-page":"1","volume":"7","author":"H. Xiong","year":"2006","unstructured":"Xiong, H., Chen, X.-W.: Kernel-Based Distance Metric Learning for Microarray Data Classification. BMC Bioinformatics\u00a07(299), 1\u201311 (2006)","journal-title":"BMC Bioinformatics"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks \u2013 ICANN 2009"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-04277-5_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,22]],"date-time":"2019-05-22T19:56:26Z","timestamp":1558554986000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-04277-5_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009]]},"ISBN":["9783642042768","9783642042775"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-04277-5_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2009]]}}}