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10.1515/sagmb-2013-0053 http://scihub22266oqcxt.onion/10.1515/sagmb-2013-0053 C4775275!4775275!24918456     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24918456&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Appl+Genet+Mol+Biol 2014 ; 13 (4): 477-96 Nephropedia Template TP {{tp|p=24918456|t=2014. Multiclass cancer classification based on gene expression comparison |pdf=|usr=}}{{24918456}} gab.com Text Multiclass cancer classification based on gene expression comparison JO: Stat Appl Genet Mol Biol http://www.kidney.de/pget.php?&tw=1&pmid=24918456 #FOAvip As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analyses, microarray-based cancer classification comprising multiple discriminatory molecular markers is an emerging trend. Such multiclass classification problems pose new methodological and computational challenges for developing novel and effective statistical approaches. In this paper, we introduce a new approach for classifying multiple disease states associated with cancer based on gene expression profiles. Our method focuses on detecting small sets of genes in which the relative comparison of their expression values leads to class discrimination. For an m-class problem, the classification rule typically depends on a small number of m-gene sets, which provide transparent decision boundaries and allow for potential biological interpretations. We first test our approach on seven common gene expression datasets and compare it with popular classification methods including support vector machines and random forests. We then consider an extremely large cohort of leukemia cancer to further assess its effectiveness. In both experiments, our method yields comparable or even better results to benchmark classifiers. In addition, we demonstrate that our approach can integrate pathway analysis of gene expression to provide accurate and biological meaningful classification. Twit Text FOAVip Multiclass cancer classification based on gene expression comparison ????Stat Appl Genet Mol Biol http://www.kidney.de/pget.php?&tw=1&pmid=24918456 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24918456 #FOAvip English Wikipedia <ref>{{Cite pmid|24918456}}</ref> Multiclass cancer classification based on gene expression comparison #MMPMID24918456 Yang S; Naiman DQ Stat Appl Genet Mol Biol 2014[Aug]; 13 (4): 477-96 PMID24918456show ga As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analyses, microarray-based cancer classification comprising multiple discriminatory molecular markers is an emerging trend. Such multiclass classification problems pose new methodological and computational challenges for developing novel and effective statistical approaches. In this paper, we introduce a new approach for classifying multiple disease states associated with cancer based on gene expression profiles. Our method focuses on detecting small sets of genes in which the relative comparison of their expression values leads to class discrimination. For an m-class problem, the classification rule typically depends on a small number of m-gene sets, which provide transparent decision boundaries and allow for potential biological interpretations. We first test our approach on seven common gene expression datasets and compare it with popular classification methods including support vector machines and random forests. We then consider an extremely large cohort of leukemia cancer to further assess its effectiveness. In both experiments, our method yields comparable or even better results to benchmark classifiers. In addition, we demonstrate that our approach can integrate pathway analysis of gene expression to provide accurate and biological meaningful classification. äMulticlass cancer classification based on gene expression comparison (epmc).pdf DeepDyve Pubget Overpricing