FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2945586064> ?p ?o ?g. }

• W2945586064 endingPage "17" @default.
• W2945586064 startingPage "5" @default.
• W2945586064 abstract "Clinical lipidomics using mass spectrometry (MS) is important to support discovery of biomarkers for diagnosis and understanding the pathophysiology of diseases. Frequently, lipidomics data from clinical studies have large variations among individuals because the human metabolome/lipidome is strongly influenced by genotype, daily activity, diet and gut flora. This inter-personal variability makes data analysis more complex and normally requires a large cohort for robust statistical analysis. Crossover designed experiments treat each subject as his or her own control, thereby reducing the between-subject variability, such that the effects of exposure/treatment are more likely to be identified when using a relatively small number of subjects. This design repeatedly samples an individual when crossing over from one treatment/exposure to another during the course of the study. The acquired datasets have a distinct data structure resulting from repeated longitudinal measurements. A variety of statistical methods are used in published crossover studies, but many appear to ignore the data structure inherent in the experimental design. An appropriate data analysis approach is critical to discovering robust clinical biomarkers. Hereby, we summarize the statistical methodologies suitable for clinical lipidomics studies using crossover design. To help understand and apply these methods to practical cases, we focused on the general concepts of statistical models in the context of analysis of metabolomics data without spending too much effort on mathematical details. Importantly, we aim to evaluate these methods and provide suggestions for data analysis and biomarker discovery. We applied the discussed methods on a MS-based lipidomics dataset from a double-blind random crossover designed clinical dietary intervention study. The strength and potential pitfalls of each method are briefly discussed and a suggestion for analytic workflow proposed." @default.
• W2945586064 created "2019-05-29" @default.
• W2945586064 creator A5001617881 @default.
• W2945586064 creator A5004357167 @default.
• W2945586064 creator A5036804110 @default.
• W2945586064 creator A5066126602 @default.
• W2945586064 creator A5075354948 @default.
• W2945586064 creator A5091406001 @default.
• W2945586064 date "2019-08-01" @default.
• W2945586064 modified "2023-10-12" @default.
• W2945586064 title "Data analysis of MS-based clinical lipidomics studies with crossover design: A tutorial mini-review of statistical methods" @default.
• W2945586064 cites W184439366 @default.
• W2945586064 cites W1965315089 @default.
• W2945586064 cites W1985121360 @default.
• W2945586064 cites W1985752966 @default.
• W2945586064 cites W1992549770 @default.
• W2945586064 cites W2006918106 @default.
• W2945586064 cites W2009033874 @default.
• W2945586064 cites W2013194198 @default.
• W2945586064 cites W2017807285 @default.
• W2945586064 cites W2029266629 @default.
• W2945586064 cites W2032217388 @default.
• W2945586064 cites W2035421116 @default.
• W2945586064 cites W2037833102 @default.
• W2945586064 cites W2039514112 @default.
• W2945586064 cites W2040961545 @default.
• W2945586064 cites W2042475696 @default.
• W2945586064 cites W2050135062 @default.
• W2945586064 cites W2051107028 @default.
• W2945586064 cites W2054324592 @default.
• W2945586064 cites W2057235898 @default.
• W2945586064 cites W2060733138 @default.
• W2945586064 cites W2062551369 @default.
• W2945586064 cites W2067542637 @default.
• W2945586064 cites W2096007495 @default.
• W2945586064 cites W2096976516 @default.
• W2945586064 cites W2104787245 @default.
• W2945586064 cites W2110284643 @default.
• W2945586064 cites W2118524679 @default.
• W2945586064 cites W2124911115 @default.
• W2945586064 cites W2127077606 @default.
• W2945586064 cites W2128061933 @default.
• W2945586064 cites W2130227372 @default.
• W2945586064 cites W2130648981 @default.
• W2945586064 cites W2134741034 @default.
• W2945586064 cites W2137430824 @default.
• W2945586064 cites W2146512944 @default.
• W2945586064 cites W2147951581 @default.
• W2945586064 cites W2162407506 @default.
• W2945586064 cites W2168526937 @default.
• W2945586064 cites W2179811767 @default.
• W2945586064 cites W2260416838 @default.
• W2945586064 cites W2261586976 @default.
• W2945586064 cites W2315147752 @default.
• W2945586064 cites W2460469530 @default.
• W2945586064 cites W2462877134 @default.
• W2945586064 cites W2463168472 @default.
• W2945586064 cites W2475097768 @default.
• W2945586064 cites W2512855971 @default.
• W2945586064 cites W2547670829 @default.
• W2945586064 cites W2565040615 @default.
• W2945586064 cites W2574422337 @default.
• W2945586064 cites W2600948559 @default.
• W2945586064 cites W2605318983 @default.
• W2945586064 cites W2613279579 @default.
• W2945586064 cites W2730028457 @default.
• W2945586064 cites W2736575297 @default.
• W2945586064 cites W2744623430 @default.
• W2945586064 cites W2744648892 @default.
• W2945586064 cites W2746051823 @default.
• W2945586064 cites W2749070449 @default.
• W2945586064 cites W2751936835 @default.
• W2945586064 cites W2765705157 @default.
• W2945586064 cites W2767372697 @default.
• W2945586064 cites W2767683865 @default.
• W2945586064 cites W2795506919 @default.
• W2945586064 cites W2797419647 @default.
• W2945586064 cites W2885260563 @default.
• W2945586064 cites W2891178556 @default.
• W2945586064 cites W2891181795 @default.
• W2945586064 cites W3192085238 @default.
• W2945586064 doi "https://doi.org/10.1016/j.clinms.2019.05.002" @default.
• W2945586064 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8620525" @default.
• W2945586064 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34841080" @default.
• W2945586064 hasPublicationYear "2019" @default.
• W2945586064 type Work @default.
• W2945586064 sameAs 2945586064 @default.
• W2945586064 citedByCount "11" @default.
• W2945586064 countsByYear W29455860642020 @default.
• W2945586064 countsByYear W29455860642021 @default.
• W2945586064 countsByYear W29455860642022 @default.
• W2945586064 countsByYear W29455860642023 @default.
• W2945586064 crossrefType "journal-article" @default.
• W2945586064 hasAuthorship W2945586064A5001617881 @default.
• W2945586064 hasAuthorship W2945586064A5004357167 @default.
• W2945586064 hasAuthorship W2945586064A5036804110 @default.
• W2945586064 hasAuthorship W2945586064A5066126602 @default.
• W2945586064 hasAuthorship W2945586064A5075354948 @default.

• next