Matches in SemOpenAlex for { <https://semopenalex.org/work/W4303470625> ?p ?o ?g. }
- W4303470625 endingPage "145" @default.
- W4303470625 startingPage "121" @default.
- W4303470625 abstract "Metabolomics data extraction may include peak deconvolution, alignment, and integration. The data extraction from CEMS spectra can usually be completed by a software designed for the extraction of liquid chromatography–mass spectrometry (MS) spectra. The purpose of data preprocessing is to preliminarily adjust the obtained data to facilitate the following statistical analysis. Pre-acquisition normalization is relevant more to experimental setups than data processing, so this chapter discusses post-acquisition normalization, which mainly focuses on the data itself. Statistical analysis is the most important step in the processing of metabolomics data. The chapter also discusses some common statistical methods. One of the most significant differences between the data processing of proteomics and metabolomics is the identification of compounds. Metabolite identification usually starts from searching against databases. The Human Metabolome Database, METLIN database, and MassBank contain comprehensive information for many metabolites, including experimental and predicted MS/MS spectra obtained at multiple collision energies." @default.
- W4303470625 created "2022-10-08" @default.
- W4303470625 creator A5030083769 @default.
- W4303470625 creator A5036450315 @default.
- W4303470625 creator A5062001426 @default.
- W4303470625 creator A5090571828 @default.
- W4303470625 date "2022-10-07" @default.
- W4303470625 modified "2023-09-26" @default.
- W4303470625 title "Data Processing in Metabolomics Capillary Electrophoresis–Mass Spectrometry" @default.
- W4303470625 cites W1482087757 @default.
- W4303470625 cites W1975775976 @default.
- W4303470625 cites W1985516654 @default.
- W4303470625 cites W1987972238 @default.
- W4303470625 cites W1989299822 @default.
- W4303470625 cites W199584104 @default.
- W4303470625 cites W1996701734 @default.
- W4303470625 cites W2001585824 @default.
- W4303470625 cites W2018507034 @default.
- W4303470625 cites W2023655541 @default.
- W4303470625 cites W2030402758 @default.
- W4303470625 cites W2042898995 @default.
- W4303470625 cites W2045619021 @default.
- W4303470625 cites W2048914567 @default.
- W4303470625 cites W2059327215 @default.
- W4303470625 cites W2062551369 @default.
- W4303470625 cites W2063958293 @default.
- W4303470625 cites W2079529928 @default.
- W4303470625 cites W2081465448 @default.
- W4303470625 cites W2084375915 @default.
- W4303470625 cites W2085504103 @default.
- W4303470625 cites W2089181989 @default.
- W4303470625 cites W2104787245 @default.
- W4303470625 cites W2124911115 @default.
- W4303470625 cites W2145445983 @default.
- W4303470625 cites W2146779833 @default.
- W4303470625 cites W2166366638 @default.
- W4303470625 cites W2195361054 @default.
- W4303470625 cites W2468305017 @default.
- W4303470625 cites W2565482980 @default.
- W4303470625 cites W2725171488 @default.
- W4303470625 cites W2767683865 @default.
- W4303470625 cites W2809895957 @default.
- W4303470625 cites W2887543466 @default.
- W4303470625 cites W2951837249 @default.
- W4303470625 cites W2974361058 @default.
- W4303470625 cites W2976451393 @default.
- W4303470625 cites W3003572520 @default.
- W4303470625 cites W3091668801 @default.
- W4303470625 cites W3159995035 @default.
- W4303470625 cites W4239510810 @default.
- W4303470625 doi "https://doi.org/10.1002/9783527833092.ch4" @default.
- W4303470625 hasPublicationYear "2022" @default.
- W4303470625 type Work @default.
- W4303470625 citedByCount "0" @default.
- W4303470625 crossrefType "other" @default.
- W4303470625 hasAuthorship W4303470625A5030083769 @default.
- W4303470625 hasAuthorship W4303470625A5036450315 @default.
- W4303470625 hasAuthorship W4303470625A5062001426 @default.
- W4303470625 hasAuthorship W4303470625A5090571828 @default.
- W4303470625 hasConcept C104317684 @default.
- W4303470625 hasConcept C10551718 @default.
- W4303470625 hasConcept C111919701 @default.
- W4303470625 hasConcept C124101348 @default.
- W4303470625 hasConcept C135870905 @default.
- W4303470625 hasConcept C136886441 @default.
- W4303470625 hasConcept C138827492 @default.
- W4303470625 hasConcept C144024400 @default.
- W4303470625 hasConcept C153180895 @default.
- W4303470625 hasConcept C154945302 @default.
- W4303470625 hasConcept C162356407 @default.
- W4303470625 hasConcept C162984825 @default.
- W4303470625 hasConcept C163985040 @default.
- W4303470625 hasConcept C185592680 @default.
- W4303470625 hasConcept C19165224 @default.
- W4303470625 hasConcept C21565614 @default.
- W4303470625 hasConcept C2777466982 @default.
- W4303470625 hasConcept C2779473830 @default.
- W4303470625 hasConcept C34736171 @default.
- W4303470625 hasConcept C41008148 @default.
- W4303470625 hasConcept C43617362 @default.
- W4303470625 hasConcept C46111723 @default.
- W4303470625 hasConcept C55493867 @default.
- W4303470625 hasConcept C77088390 @default.
- W4303470625 hasConceptScore W4303470625C104317684 @default.
- W4303470625 hasConceptScore W4303470625C10551718 @default.
- W4303470625 hasConceptScore W4303470625C111919701 @default.
- W4303470625 hasConceptScore W4303470625C124101348 @default.
- W4303470625 hasConceptScore W4303470625C135870905 @default.
- W4303470625 hasConceptScore W4303470625C136886441 @default.
- W4303470625 hasConceptScore W4303470625C138827492 @default.
- W4303470625 hasConceptScore W4303470625C144024400 @default.
- W4303470625 hasConceptScore W4303470625C153180895 @default.
- W4303470625 hasConceptScore W4303470625C154945302 @default.
- W4303470625 hasConceptScore W4303470625C162356407 @default.
- W4303470625 hasConceptScore W4303470625C162984825 @default.
- W4303470625 hasConceptScore W4303470625C163985040 @default.
- W4303470625 hasConceptScore W4303470625C185592680 @default.
- W4303470625 hasConceptScore W4303470625C19165224 @default.