about
Integrated analysis of seaweed components during seasonal fluctuation by data mining across heterogeneous chemical measurements with network visualization.Pretreatment and integrated analysis of spectral data reveal seaweed similarities based on chemical diversity.Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals.Fragment Assembly Approach Based on Graph/Network Theory with Quantum Chemistry Verifications for Assigning Multidimensional NMR Signals in Metabolite Mixtures
P50
description
researcher ORCID ID = 0000-0002-5700-688X
@en
wetenschapper
@nl
name
Kengo Ito
@ast
Kengo Ito
@en
Kengo Ito
@es
Kengo Ito
@nl
type
label
Kengo Ito
@ast
Kengo Ito
@en
Kengo Ito
@es
Kengo Ito
@nl
prefLabel
Kengo Ito
@ast
Kengo Ito
@en
Kengo Ito
@es
Kengo Ito
@nl
P108
P31
P496
0000-0002-5700-688X