TY  - BOOK
AU  - Hanhineva, Kati||Van der Hooft, Justin
TI  - Metabolomics Data Processing and Data Analysis—Current Best Practices
SN  - 9783040000000
KW  - metabolic networks||mass spectral libraries||metabolite annotation||metabolomics data mapping||nontarget analysis||liquid chromatography mass spectrometry||compound identification||tandem mass spectral library||forensics||wastewater||gut microbiome||meta-omics||metagenomics||metabolomics||metabolic reconstructions||genome-scale metabolic modeling||constraint-based modeling||flux balance||host–microbiome||metabolism||global metabolomics||LC-MS||spectra processing||pathway analysis||enrichment analysis||mass spectrometry||liquid chromatography||MS spectral prediction||metabolite identification||structure-based chemical classification||rule-based fragmentation||combinatorial fragmentation||time series||PLS||NPLS||variable selection||bootstrapped-VIP||data repository||computational metabolomics||reanalysis||lipidomics||data processing||triplot||multivariate risk modeling||environmental factors||disease risk||chemical classification||in silico workflows||metabolome mining||molecular families||networking||substructures||mass spectrometry imaging||metabolomics imaging||biostatistics||ion selection algorithms||liquid chromatography high-resolution mass spectrometry||data-independent acquisition||all ion fragmentation||targeted analysis||untargeted analysis||R programming||full-scan MS/MS processing||R-MetaboList 2||liquid chromatography–mass spectrometry (LC/MS)||fragmentation (MS/MS)||data-dependent acquisition (DDA)||simulator||in silico||untargeted metabolomics||liquid chromatography–mass spectrometry (LC-MS)||experimental design||sample preparation||univariate and multivariate statistics||metabolic pathway and network analysis||LC–MS||metabolic profiling||computational statistical||unsupervised learning||supervised learning
UR  - https://mdpi.com/books/pdfview/book/4323
ER  -