{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T15:38:38Z","timestamp":1740152318104,"version":"3.37.3"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2009,7,20]],"date-time":"2009-07-20T00:00:00Z","timestamp":1248048000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"crossref","award":["2007CB311002","30700161","30570368"],"award-info":[{"award-number":["2007CB311002","30700161","30570368"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2007CB311002","30700161","30570368"],"award-info":[{"award-number":["2007CB311002","30700161","30570368"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2007CB311002","30700161","30570368"],"award-info":[{"award-number":["2007CB311002","30700161","30570368"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Advances in Bioinformatics"],"published-print":{"date-parts":[[2009,7,20]]},"abstract":"<jats:p><jats:italic>Motivation<\/jats:italic>. Independent Components Analysis (ICA) maximizes the statistical independence of the representational components of a training gene expression profiles (GEP) ensemble, but it cannot distinguish relations between the different factors, or different modes, and it is not available to high-order GEP Data Mining. In order to generalize ICA, we introduce Multilinear-ICA and apply it to tumor classification using high order GEP. Firstly, we introduce the basis conceptions and operations of tensor and recommend Support Vector Machine (SVM) classifier and Multilinear-ICA. Secondly, the higher score genes of original high order GEP are selected by using <jats:italic>t-statistics<\/jats:italic> and tabulate tensors. Thirdly, the tensors are performed by Multilinear-ICA. Finally, the SVM is used to classify the tumor subtypes. <jats:italic>Results<\/jats:italic>. To show the validity of the proposed method, we apply it to tumor classification using high order GEP. Though we only use three datasets, the experimental results show that the method is effective and feasible. Through this survey, we hope to gain some insight into the problem of high order GEP tumor classification, in aid of further developing more effective tumor classification algorithms.<\/jats:p>","DOI":"10.1155\/2009\/926450","type":"journal-article","created":{"date-parts":[[2009,7,20]],"date-time":"2009-07-20T14:59:56Z","timestamp":1248101996000},"page":"1-9","source":"Crossref","is-referenced-by-count":3,"title":["Tumor Classification Using High-Order Gene Expression Profiles Based on Multilinear ICA"],"prefix":"10.1155","volume":"2009","author":[{"given":"Ming-gang","family":"Du","sequence":"first","affiliation":[{"name":"School of Urban and Environment Science, Shanxi Normal University, Linfen, Shanxi 041004, China"}]},{"given":"Shan-Wen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China"}]},{"given":"Hong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Computer Science, Shanxi Normal University, Linfen, Shanxi 041004, China"}]}],"member":"311","reference":[{"issue":"7","key":"21","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1101\/gr.6.7.639","volume":"6","year":"1996","journal-title":"Genome Research"},{"key":"11","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1145\/381371.381384","volume":"21","year":"2001","journal-title":"ACM SIGBIO Newsletter"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2007.1006"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.96.12.6745"},{"issue":"10","key":"8","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1093\/bioinformatics\/16.10.906","volume":"16","year":"2000","journal-title":"Bioinformatics"},{"key":"30","series-title":"Lecture Notes in Computer Science","first-page":"476","volume-title":"A novel clustering analysis based on PCA and SOMs for gene expression patterns","volume":"317","year":"2004"},{"issue":"1","key":"6","first-page":"1","volume":"5","year":"2006","journal-title":"Statistical Applications in Genetics and Molecular Biology"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2006.02.006"},{"year":"2001","key":"15"},{"issue":"1","key":"19","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1093\/bioinformatics\/18.1.51","volume":"18","year":"2002","journal-title":"Bioinformatics"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl190"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1038\/sj.ejhg.5201495"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-7-290"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.2144\/000112950"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-9-244"},{"volume-title":"Multilinear independent component analysis","year":"2004","key":"28"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0709146104"},{"volume-title":"Tensor subspace analysis","year":"2005","key":"12"},{"issue":"4","key":"18","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1137\/S0895479896305696","volume":"21","year":"2000","journal-title":"SIAM Journal on Matrix Analysis and Applications"},{"year":"1996","series-title":"Johns Hopkins Studies in the Mathematical Sciences","key":"9"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.091062498"},{"year":"1998","key":"24"},{"year":"2000","key":"5"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1145\/1186785.1186794"}],"container-title":["Advances in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/archive\/2009\/926450.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/archive\/2009\/926450.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/archive\/2009\/926450.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T17:24:09Z","timestamp":1607448249000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/abi\/2009\/926450\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,7,20]]},"references-count":24,"alternative-id":["926450","926450"],"URL":"https:\/\/doi.org\/10.1155\/2009\/926450","relation":{},"ISSN":["1687-8027","1687-8035"],"issn-type":[{"type":"print","value":"1687-8027"},{"type":"electronic","value":"1687-8035"}],"subject":[],"published":{"date-parts":[[2009,7,20]]}}}