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10.3978/j.issn.2218-676X.2014.05.06 http://scihub22266oqcxt.onion/10.3978/j.issn.2218-676X.2014.05.06 C4692276!4692276 !26719835     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26719835 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Transl+Cancer+Res 2014 ; 3 (3 ): 182-190 Nephropedia Template TP {{tp|p=26719835 |t=2014. Sparse principal component analysis in cancer research |pdf=|usr=}}{{26719835 }} gab.com Text Sparse principal component analysis in cancer research JO: Transl Cancer Res http://www.kidney.de/pget.php?&tw=1&pmid=26719835 #FOAvip A critical challenging component in analyzing high-dimensional data in cancer research is how to reduce the dimension of data and how to extract relevant features. Sparse principal component analysis (PCA) is a powerful statistical tool that could help reduce data dimension and select important variables simultaneously. In this paper, we review several approaches for sparse PCA, including variance maximization (VM), reconstruction error minimization (REM), singular value decomposition (SVD), and probabilistic modeling (PM) approaches. A simulation study is conducted to compare PCA and the sparse PCAs. An example using a published gene signature in a lung cancer dataset is used to illustrate the potential application of sparse PCAs in cancer research. Twit Text FOAVip Sparse principal component analysis in cancer research ????Transl Cancer Res http://www.kidney.de/pget.php?&tw=1&pmid=26719835 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26719835 #FOAvip English Wikipedia <ref>{{Cite pmid|26719835 }}</ref> Sparse principal component analysis in cancer research #MMPMID26719835 Hsu YL ; Huang PY ; Chen DT Transl Cancer Res 2014[Jun]; 3 (3 ): 182-190 PMID26719835 show ga A critical challenging component in analyzing high-dimensional data in cancer research is how to reduce the dimension of data and how to extract relevant features. Sparse principal component analysis (PCA) is a powerful statistical tool that could help reduce data dimension and select important variables simultaneously. In this paper, we review several approaches for sparse PCA, including variance maximization (VM), reconstruction error minimization (REM), singular value decomposition (SVD), and probabilistic modeling (PM) approaches. A simulation study is conducted to compare PCA and the sparse PCAs. An example using a published gene signature in a lung cancer dataset is used to illustrate the potential application of sparse PCAs in cancer research. äSparse principal component analysis in cancer research (epmc).pdf DeepDyve Pubget Overpricing