Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385364706> ?p ?o ?g. }
- W4385364706 endingPage "4330" @default.
- W4385364706 startingPage "4318" @default.
- W4385364706 abstract "There is a wide variety of autoimmune diseases (ADs) with complex pathogenesis and their accurate diagnosis is difficult to achieve because of their vague symptoms. Metabolomics has been proven to be an efficient tool in the analysis of metabolic disorders to provide clues about the mechanism and diagnosis of diseases. Previous studies of the metabolomics analysis of ADs were not competent in their discrimination. Herein, a liquid chromatography tandem mass spectrometry (LC-MS) strategy combined with machine learning is proposed for the discrimination and classification of ADs. Urine and serum samples were collected from 267 subjects consisting of 127 healthy controls (HC) and 140 AD patients, including those with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), sicca syndrome (SS), ankylosing spondylitis (AS), systemic scleroderma (SSc) and connective tissue disease (CTD). Machine learning algorithms were encoded for the discrimination and classification of ADs with metabolomic patterns obtained by LC-MS, and satisfactory results were achieved. Notably, urine samples exhibited higher accuracy for disease differentiation and triage than serum samples. Apart from that, differential metabolites were selected and metabolite panels were evaluated to demonstrate their representativeness. Metabolic dysregulations were also investigated to gain more knowledge about the pathogenesis of ADs. This research provides a promising method for the application of metabolomics combined with machine learning in precision medicine." @default.
- W4385364706 created "2023-07-29" @default.
- W4385364706 creator A5006456247 @default.
- W4385364706 creator A5018889205 @default.
- W4385364706 creator A5027440639 @default.
- W4385364706 creator A5034328902 @default.
- W4385364706 creator A5046784859 @default.
- W4385364706 creator A5052787170 @default.
- W4385364706 creator A5058010200 @default.
- W4385364706 creator A5065074839 @default.
- W4385364706 creator A5066283714 @default.
- W4385364706 date "2023-01-01" @default.
- W4385364706 modified "2023-10-17" @default.
- W4385364706 title "Machine learning encodes urine and serum metabolic patterns for autoimmune diseases discrimination, classification and metabolic dysregulation analysis" @default.
- W4385364706 cites W1573123443 @default.
- W4385364706 cites W2014949148 @default.
- W4385364706 cites W2025813437 @default.
- W4385364706 cites W2050766279 @default.
- W4385364706 cites W2051173337 @default.
- W4385364706 cites W2051549685 @default.
- W4385364706 cites W2057645759 @default.
- W4385364706 cites W2063491954 @default.
- W4385364706 cites W2074859589 @default.
- W4385364706 cites W2101130417 @default.
- W4385364706 cites W2105406990 @default.
- W4385364706 cites W2135812798 @default.
- W4385364706 cites W2147259528 @default.
- W4385364706 cites W2155680682 @default.
- W4385364706 cites W2162589030 @default.
- W4385364706 cites W2261782925 @default.
- W4385364706 cites W2474066613 @default.
- W4385364706 cites W2548554376 @default.
- W4385364706 cites W2606913462 @default.
- W4385364706 cites W2625912244 @default.
- W4385364706 cites W2735584664 @default.
- W4385364706 cites W2736575639 @default.
- W4385364706 cites W2768506483 @default.
- W4385364706 cites W2775131478 @default.
- W4385364706 cites W2900850467 @default.
- W4385364706 cites W2902532424 @default.
- W4385364706 cites W2907800538 @default.
- W4385364706 cites W2912737431 @default.
- W4385364706 cites W2952427554 @default.
- W4385364706 cites W2988238865 @default.
- W4385364706 cites W3003324155 @default.
- W4385364706 cites W3004554159 @default.
- W4385364706 cites W3028789162 @default.
- W4385364706 cites W3088203083 @default.
- W4385364706 cites W3093087868 @default.
- W4385364706 cites W3099767975 @default.
- W4385364706 cites W3165159002 @default.
- W4385364706 cites W3176250497 @default.
- W4385364706 cites W3176672033 @default.
- W4385364706 cites W3214776688 @default.
- W4385364706 cites W4200163545 @default.
- W4385364706 cites W4200292058 @default.
- W4385364706 cites W4205830567 @default.
- W4385364706 cites W4211072505 @default.
- W4385364706 cites W4214611162 @default.
- W4385364706 cites W4281648813 @default.
- W4385364706 cites W4292550249 @default.
- W4385364706 cites W4294883950 @default.
- W4385364706 cites W4297200598 @default.
- W4385364706 cites W4312058275 @default.
- W4385364706 cites W4313530732 @default.
- W4385364706 cites W4317538266 @default.
- W4385364706 cites W4317666324 @default.
- W4385364706 cites W4317831335 @default.
- W4385364706 cites W4318754928 @default.
- W4385364706 cites W4319069426 @default.
- W4385364706 cites W4321507040 @default.
- W4385364706 cites W4322757513 @default.
- W4385364706 cites W4322763872 @default.
- W4385364706 cites W4323810846 @default.
- W4385364706 cites W4327550240 @default.
- W4385364706 cites W4375956057 @default.
- W4385364706 doi "https://doi.org/10.1039/d3an01051a" @default.
- W4385364706 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37547947" @default.
- W4385364706 hasPublicationYear "2023" @default.
- W4385364706 type Work @default.
- W4385364706 citedByCount "0" @default.
- W4385364706 crossrefType "journal-article" @default.
- W4385364706 hasAuthorship W4385364706A5006456247 @default.
- W4385364706 hasAuthorship W4385364706A5018889205 @default.
- W4385364706 hasAuthorship W4385364706A5027440639 @default.
- W4385364706 hasAuthorship W4385364706A5034328902 @default.
- W4385364706 hasAuthorship W4385364706A5046784859 @default.
- W4385364706 hasAuthorship W4385364706A5052787170 @default.
- W4385364706 hasAuthorship W4385364706A5058010200 @default.
- W4385364706 hasAuthorship W4385364706A5065074839 @default.
- W4385364706 hasAuthorship W4385364706A5066283714 @default.
- W4385364706 hasConcept C126322002 @default.
- W4385364706 hasConcept C203014093 @default.
- W4385364706 hasConcept C21565614 @default.
- W4385364706 hasConcept C2777477808 @default.
- W4385364706 hasConcept C2777575956 @default.
- W4385364706 hasConcept C2779075594 @default.
- W4385364706 hasConcept C2779134260 @default.