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10.1007/s12551-011-0045-8 http://scihub22266oqcxt.onion/10.1007/s12551-011-0045-8 C5418393!5418393!28510234     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biophys+Rev 2011 ; 3 (1): 47-52 Nephropedia Template TP {{tp|p=28510234|t=2011. Deriving biomedical diagnostics from NMR spectroscopic data |pdf=|usr=}}{{28510234}} gab.com Text Deriving biomedical diagnostics from NMR spectroscopic data JO: Biophys Rev http://www.kidney.de/pget.php?&tw=1&pmid=28510234 #FOAvip Biomedical spectroscopic experiments generate large volumes of data. For accurate, robust diagnostic tools the data must be analyzed for only a few characteristic observations per subject, and a large number of subjects must be studied. We describe here two of the current data analytic approaches applied to this problem: SIMCA (principal component analysis, partial least squares), and the statistical classification strategy (SCS). We demonstrate the application of the SCS by three examples of its use in analyzing 1H NMR spectra: screening for colon cancer, characterization of thyroid cancer, and distinguishing cancer from cholangitis in the biliary tract. Twit Text FOAVip Deriving biomedical diagnostics from NMR spectroscopic data ????Biophys Rev http://www.kidney.de/pget.php?&tw=1&pmid=28510234 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28510234 #FOAvip English Wikipedia <ref>{{Cite pmid|28510234}}</ref> Deriving biomedical diagnostics from NMR spectroscopic data #MMPMID28510234 Smith ICP; Somorjai RL Biophys Rev 2011[Mar]; 3 (1): 47-52 PMID28510234show ga Biomedical spectroscopic experiments generate large volumes of data. For accurate, robust diagnostic tools the data must be analyzed for only a few characteristic observations per subject, and a large number of subjects must be studied. We describe here two of the current data analytic approaches applied to this problem: SIMCA (principal component analysis, partial least squares), and the statistical classification strategy (SCS). We demonstrate the application of the SCS by three examples of its use in analyzing 1H NMR spectra: screening for colon cancer, characterization of thyroid cancer, and distinguishing cancer from cholangitis in the biliary tract. äDeriving biomedical diagnostics from NMR spectroscopic data (epmc).pdf DeepDyve Pubget Overpricing