000 01360 a2200385 4500
005 20250518004838.0
264 0 _c20190221
008 201902s 0 0 eng d
022 _a2515-4184
024 7 _a10.1039/c8mo00095f
_2doi
040 _aNLM
_beng
_cNLM
100 1 _aFürtauer, Lisa
245 0 0 _aCombined multivariate analysis and machine learning reveals a predictive module of metabolic stress response in Arabidopsis thaliana.
_h[electronic resource]
260 _bMolecular omics
_c12 2018
300 _a437-449 p.
_bdigital
500 _aPublication Type: Journal Article; Research Support, Non-U.S. Gov't
650 0 4 _aAnalysis of Variance
650 0 4 _aArabidopsis
_xgenetics
650 0 4 _aCell Line
650 0 4 _aChlorophyll
_xmetabolism
650 0 4 _aComputational Biology
_xmethods
650 0 4 _aMachine Learning
650 0 4 _aMetabolomics
_xmethods
650 0 4 _aMultivariate Analysis
650 0 4 _aMutation
650 0 4 _aPhosphoglucomutase
_xdeficiency
650 0 4 _aStress, Physiological
650 0 4 _aSucrose
_xmetabolism
700 1 _aPschenitschnigg, Alice
700 1 _aScharkosi, Helene
700 1 _aWeckwerth, Wolfram
700 1 _aNägele, Thomas
773 0 _tMolecular omics
_gvol. 14
_gno. 6
_gp. 437-449
856 4 0 _uhttps://doi.org/10.1039/c8mo00095f
_zAvailable from publisher's website
999 _c29003970
_d29003970