Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220943622> ?p ?o ?g. }
Showing items 1 to 54 of
54
with 100 items per page.
- W4220943622 endingPage "7" @default.
- W4220943622 startingPage "1" @default.
- W4220943622 abstract "Discovering protein biomarkers is one of the important issues in biomedical researches. The enzymelinked immunosorbent assay (ELISA) is one of the traditional techniques for protein quantitation. Recently, the multiple reaction monitoring (MRM) mass spectrometry has been proposed as a new method for protein quantification and has been popular as an alternative to ELISA. However, not many analysis methods are available yet to analyse MRM data. Linear mixed models (LMMs) are effective in analysing MRM data. MSstats is one of the most widely used tools for MRM data analysis which is based on the LMMs. MSstats is well implemented on Skyline program and R programming language. However, LMMs often provide various significance results depending on model specification. Thus, sometimes it would be difficult to specify a right LMM for the analysis of MRM data. In this paper, we systematically investigated the effect of model specification on significance of proteins through simulation studies. Our results provide a practical guideline of using LMMs for MRM data analysis." @default.
- W4220943622 created "2022-04-03" @default.
- W4220943622 creator A5021812132 @default.
- W4220943622 creator A5029694118 @default.
- W4220943622 date "2022-03-19" @default.
- W4220943622 modified "2023-10-14" @default.
- W4220943622 title "Linear Mixed Model Approach to Protein Significance Analysis" @default.
- W4220943622 cites W1257453011 @default.
- W4220943622 cites W1963527734 @default.
- W4220943622 cites W2006202822 @default.
- W4220943622 cites W2017792346 @default.
- W4220943622 cites W2065839570 @default.
- W4220943622 cites W2085802434 @default.
- W4220943622 cites W2095793137 @default.
- W4220943622 cites W2120527469 @default.
- W4220943622 cites W2130652959 @default.
- W4220943622 cites W2139339310 @default.
- W4220943622 cites W2154670730 @default.
- W4220943622 cites W2166546590 @default.
- W4220943622 doi "https://doi.org/10.37394/232022.2022.2.1" @default.
- W4220943622 hasPublicationYear "2022" @default.
- W4220943622 type Work @default.
- W4220943622 citedByCount "0" @default.
- W4220943622 crossrefType "journal-article" @default.
- W4220943622 hasAuthorship W4220943622A5021812132 @default.
- W4220943622 hasAuthorship W4220943622A5029694118 @default.
- W4220943622 hasBestOaLocation W42209436221 @default.
- W4220943622 hasConcept C119857082 @default.
- W4220943622 hasConcept C124101348 @default.
- W4220943622 hasConcept C153720581 @default.
- W4220943622 hasConcept C41008148 @default.
- W4220943622 hasConceptScore W4220943622C119857082 @default.
- W4220943622 hasConceptScore W4220943622C124101348 @default.
- W4220943622 hasConceptScore W4220943622C153720581 @default.
- W4220943622 hasConceptScore W4220943622C41008148 @default.
- W4220943622 hasLocation W42209436221 @default.
- W4220943622 hasOpenAccess W4220943622 @default.
- W4220943622 hasPrimaryLocation W42209436221 @default.
- W4220943622 hasRelatedWork W2347219288 @default.
- W4220943622 hasRelatedWork W2350741829 @default.
- W4220943622 hasRelatedWork W2358668433 @default.
- W4220943622 hasRelatedWork W2366221835 @default.
- W4220943622 hasRelatedWork W2376932109 @default.
- W4220943622 hasRelatedWork W2382290278 @default.
- W4220943622 hasRelatedWork W2390279801 @default.
- W4220943622 hasRelatedWork W2748952813 @default.
- W4220943622 hasRelatedWork W2889453578 @default.
- W4220943622 hasRelatedWork W2899084033 @default.
- W4220943622 hasVolume "2" @default.
- W4220943622 isParatext "false" @default.
- W4220943622 isRetracted "false" @default.
- W4220943622 workType "article" @default.