LEADER 01729nam0-2200553---450- 001 990000055860203316 005 20050629132121.0 010 $a0-521-62289-1 035 $a0005586 035 $aUSA010005586 035 $a(ALEPH)000005586USA01 035 $a0005586 100 $a20000914d1998----|||y0itay0103----ba 101 0 $aeng 102 $aGB 105 $a||||||||001|y 200 1 $aCasual asymmetric$fDaniel M. Hausman 210 $aCambridge$cCambridge University Press$d1998 215 $aXV, 300 p.$d24 cm 225 2 $aCambridge studies in probability, induction, and decision theory 410 0$12001$aCambridge studies in probability, induction, and decision theory 606 $aCausalità 606 $aFilosofia della scienza 676 $a122 700 1$aHAUSMAN,$bDaniel M.$0121751 801 $aIT$bSALBC$gISBD 912 $a990000055860203316 951 $aII.6. 786 (IV C 2915)$b150229 L.M.$cIV C$d00026535 959 $aBK 969 $aUMA 979 $c20000914$lUSA01$h1725 979 $c20000919$lUSA01$h1047 979 $c20000919$lUSA01$h1520 979 $c20001019$lUSA01$h1055 979 $c20001019$lUSA01$h1452 979 $c20001019$lUSA01$h1500 979 $c20001019$lUSA01$h1537 979 $c20001024$lUSA01$h1513 979 $c20001027$lUSA01$h1518 979 $c20001027$lUSA01$h1522 979 $c20001110$lUSA01$h1709 979 $c20001124$lUSA01$h1207 979 $aPATTY$b90$c20011016$lUSA01$h1932 979 $c20020403$lUSA01$h1613 979 $aPATRY$b90$c20040406$lUSA01$h1605 979 $aCOPAT4$b90$c20050629$lUSA01$h1321 979 $aCOPAT4$b90$c20050629$lUSA01$h1321 996 $aCasual asymmetric$91516360 997 $aUNISA LEADER 03362nam 2200793z- 450 001 9910585936303321 005 20220812 035 $a(CKB)5600000000483122 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/91148 035 $a(oapen)doab91148 035 $a(EXLCZ)995600000000483122 100 $a20202208d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aResearch in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (136 p.) 311 08$a3-0365-4553-0 311 08$a3-0365-4554-9 330 $aMetabolomics entails the comprehensive characterization of the ensemble of endogenous and exogenous metabolites present in a biological specimen. Metabolites represent, at the same time, the downstream output of the genome and the upstream input from various external factors, such as the environment, lifestyle, and diet. Therefore, in the last few years, metabolomic phenotyping has provided unique insights into the fundamental and molecular causes of several physiological and pathophysiological conditions. In parallel, metabolomics has been demonstrating an emerging role in monitoring the influence of different manufacturing procedures on food quality and food safety. In light of the above, this collection includes the latest research from various fields of NMR-based metabolomics applications ranging from biomedicine to data mining and food chemistry. 517 $aResearch in Metabolomics via Nuclear Magnetic Resonance Spectroscopy 606 $aBiochemistry$2bicssc 606 $aBiology, life sciences$2bicssc 606 $aResearch and information: general$2bicssc 610 $aartificial intelligence 610 $abiomarkers 610 $aclustering 610 $acoffee beans 610 $acoffee processing 610 $acoffee varieties 610 $acolorectal cancer 610 $aCOVID-19 610 $adeep learning 610 $aEOS 610 $aexhaled breath condensate 610 $ahealth science 610 $ahuman plasma 610 $aLOS 610 $amachine learning 610 $ametabolomics 610 $an/a 610 $ananoparticles exposure 610 $aneonatal sepsis 610 $aNMR 610 $aNMR metabolomics 610 $aNMR spectroscopy 610 $anuclear magnetic resonance 610 $anuclear magnetic resonance spectroscopy 610 $aphenotyping 610 $apost-harvest treatment 610 $apreterm birth 610 $arelapse 610 $asurgery 615 7$aBiochemistry 615 7$aBiology, life sciences 615 7$aResearch and information: general 700 $aVignoli$b Alessia$4edt$0479151 702 $aMeoni$b Gaia$4edt 702 $aTenori$b Leonardo$4edt 702 $aVignoli$b Alessia$4oth 702 $aMeoni$b Gaia$4oth 702 $aTenori$b Leonardo$4oth 906 $aBOOK 912 $a9910585936303321 996 $aResearch in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry$93012098 997 $aUNINA