Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

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       Transl+Cancer+Res 2014 ; 3 (3): 182-90 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): 182-90 PMID26719835show 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