Matches in SemOpenAlex for { <https://semopenalex.org/work/W3183201532> ?p ?o ?g. }
- W3183201532 endingPage "e0255240" @default.
- W3183201532 startingPage "e0255240" @default.
- W3183201532 abstract "Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into metabolomic data analysis. In this paper, we introduce one such recently developed technique called aggregate knockoff filtering to untargeted metabolomic analysis. When applied to a publicly available dataset, aggregate knockoff filtering combined with typical p-value filtering improves the number of significantly changing metabolites by 25% when compared to conventional untargeted metabolomic data processing. By using this method, features that would normally not be extracted under standard processing would be brought to researchers’ attention for further analysis." @default.
- W3183201532 created "2021-08-02" @default.
- W3183201532 creator A5004738943 @default.
- W3183201532 creator A5009901092 @default.
- W3183201532 creator A5023955036 @default.
- W3183201532 creator A5031840425 @default.
- W3183201532 creator A5052499295 @default.
- W3183201532 creator A5061929864 @default.
- W3183201532 creator A5077805884 @default.
- W3183201532 date "2021-07-29" @default.
- W3183201532 modified "2023-10-18" @default.
- W3183201532 title "Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn’s disease with a publicly available untargeted metabolomics dataset" @default.
- W3183201532 cites W140704821 @default.
- W3183201532 cites W1733991299 @default.
- W3183201532 cites W1975622364 @default.
- W3183201532 cites W1978885999 @default.
- W3183201532 cites W1992029609 @default.
- W3183201532 cites W1997153399 @default.
- W3183201532 cites W2002431248 @default.
- W3183201532 cites W2009462809 @default.
- W3183201532 cites W2033739475 @default.
- W3183201532 cites W2034241480 @default.
- W3183201532 cites W2062551369 @default.
- W3183201532 cites W2082213488 @default.
- W3183201532 cites W2100688872 @default.
- W3183201532 cites W2104787245 @default.
- W3183201532 cites W2108222459 @default.
- W3183201532 cites W2119862467 @default.
- W3183201532 cites W2119936926 @default.
- W3183201532 cites W2139381960 @default.
- W3183201532 cites W2143283561 @default.
- W3183201532 cites W2151872119 @default.
- W3183201532 cites W2155680682 @default.
- W3183201532 cites W2160195242 @default.
- W3183201532 cites W2204774351 @default.
- W3183201532 cites W2253470142 @default.
- W3183201532 cites W2333826462 @default.
- W3183201532 cites W2482268455 @default.
- W3183201532 cites W2512566098 @default.
- W3183201532 cites W2614180041 @default.
- W3183201532 cites W2631673659 @default.
- W3183201532 cites W2852950912 @default.
- W3183201532 cites W2901837190 @default.
- W3183201532 cites W2947189704 @default.
- W3183201532 cites W2951934944 @default.
- W3183201532 cites W2959053591 @default.
- W3183201532 cites W2963614304 @default.
- W3183201532 cites W2964046274 @default.
- W3183201532 cites W2974361058 @default.
- W3183201532 cites W2979573534 @default.
- W3183201532 cites W3003047249 @default.
- W3183201532 cites W3014006219 @default.
- W3183201532 cites W3102479286 @default.
- W3183201532 cites W2078638156 @default.
- W3183201532 doi "https://doi.org/10.1371/journal.pone.0255240" @default.
- W3183201532 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8320926" @default.
- W3183201532 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34324558" @default.
- W3183201532 hasPublicationYear "2021" @default.
- W3183201532 type Work @default.
- W3183201532 sameAs 3183201532 @default.
- W3183201532 citedByCount "3" @default.
- W3183201532 countsByYear W31832015322022 @default.
- W3183201532 countsByYear W31832015322023 @default.
- W3183201532 crossrefType "journal-article" @default.
- W3183201532 hasAuthorship W3183201532A5004738943 @default.
- W3183201532 hasAuthorship W3183201532A5009901092 @default.
- W3183201532 hasAuthorship W3183201532A5023955036 @default.
- W3183201532 hasAuthorship W3183201532A5031840425 @default.
- W3183201532 hasAuthorship W3183201532A5052499295 @default.
- W3183201532 hasAuthorship W3183201532A5061929864 @default.
- W3183201532 hasAuthorship W3183201532A5077805884 @default.
- W3183201532 hasBestOaLocation W31832015321 @default.
- W3183201532 hasConcept C119857082 @default.
- W3183201532 hasConcept C124101348 @default.
- W3183201532 hasConcept C154945302 @default.
- W3183201532 hasConcept C21565614 @default.
- W3183201532 hasConcept C41008148 @default.
- W3183201532 hasConcept C60644358 @default.
- W3183201532 hasConcept C70721500 @default.
- W3183201532 hasConcept C86803240 @default.
- W3183201532 hasConceptScore W3183201532C119857082 @default.
- W3183201532 hasConceptScore W3183201532C124101348 @default.
- W3183201532 hasConceptScore W3183201532C154945302 @default.
- W3183201532 hasConceptScore W3183201532C21565614 @default.
- W3183201532 hasConceptScore W3183201532C41008148 @default.
- W3183201532 hasConceptScore W3183201532C60644358 @default.
- W3183201532 hasConceptScore W3183201532C70721500 @default.
- W3183201532 hasConceptScore W3183201532C86803240 @default.
- W3183201532 hasFunder F4320338390 @default.
- W3183201532 hasIssue "7" @default.
- W3183201532 hasLocation W31832015321 @default.
- W3183201532 hasLocation W31832015322 @default.
- W3183201532 hasLocation W31832015323 @default.
- W3183201532 hasLocation W31832015324 @default.
- W3183201532 hasOpenAccess W3183201532 @default.
- W3183201532 hasPrimaryLocation W31832015321 @default.
- W3183201532 hasRelatedWork W2011444679 @default.
- W3183201532 hasRelatedWork W2489887948 @default.