Matches in SemOpenAlex for { <https://semopenalex.org/work/W4200292058> ?p ?o ?g. }
- W4200292058 abstract "Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, characterized by rapid progression, metastasis, and difficulty in diagnosis. However, there are no effective liquid-based testing methods available for PDAC detection. Here we introduce a minimally invasive approach that uses machine learning (ML) and lipidomics to detect PDAC. Through greedy algorithm and mass spectrum feature selection, we optimized 17 characteristic metabolites as detection features and developed a liquid chromatography-mass spectrometry-based targeted assay. In this study, 1033 patients with PDAC at various stages were examined. This approach has achieved 86.74% accuracy with an area under curve (AUC) of 0.9351 in the large external validation cohort and 85.00% accuracy with 0.9389 AUC in the prospective clinical cohort. Accordingly, single-cell sequencing, proteomics, and mass spectrometry imaging were applied and revealed notable alterations of selected lipids in PDAC tissues. We propose that the ML-aided lipidomics approach be used for early detection of PDAC." @default.
- W4200292058 created "2021-12-31" @default.
- W4200292058 creator A5001078878 @default.
- W4200292058 creator A5001605572 @default.
- W4200292058 creator A5001839093 @default.
- W4200292058 creator A5004442354 @default.
- W4200292058 creator A5004905094 @default.
- W4200292058 creator A5014224289 @default.
- W4200292058 creator A5015302215 @default.
- W4200292058 creator A5018564842 @default.
- W4200292058 creator A5024700299 @default.
- W4200292058 creator A5029987881 @default.
- W4200292058 creator A5038639426 @default.
- W4200292058 creator A5039939828 @default.
- W4200292058 creator A5050858841 @default.
- W4200292058 creator A5053880675 @default.
- W4200292058 creator A5057117256 @default.
- W4200292058 creator A5060284538 @default.
- W4200292058 creator A5060394362 @default.
- W4200292058 creator A5069994659 @default.
- W4200292058 creator A5073730933 @default.
- W4200292058 creator A5074839030 @default.
- W4200292058 creator A5079144156 @default.
- W4200292058 creator A5085694854 @default.
- W4200292058 creator A5086894930 @default.
- W4200292058 date "2021-12-24" @default.
- W4200292058 modified "2023-10-14" @default.
- W4200292058 title "Metabolic detection and systems analyses of pancreatic ductal adenocarcinoma through machine learning, lipidomics, and multi-omics" @default.
- W4200292058 cites W1543068333 @default.
- W4200292058 cites W1663973292 @default.
- W4200292058 cites W1830948261 @default.
- W4200292058 cites W1985237517 @default.
- W4200292058 cites W1995571423 @default.
- W4200292058 cites W2010916968 @default.
- W4200292058 cites W2030017878 @default.
- W4200292058 cites W2032691440 @default.
- W4200292058 cites W2034269086 @default.
- W4200292058 cites W2035618305 @default.
- W4200292058 cites W2042571564 @default.
- W4200292058 cites W2069269337 @default.
- W4200292058 cites W2069376956 @default.
- W4200292058 cites W2080752012 @default.
- W4200292058 cites W2081359173 @default.
- W4200292058 cites W2091531747 @default.
- W4200292058 cites W2091536410 @default.
- W4200292058 cites W2099023480 @default.
- W4200292058 cites W2113432838 @default.
- W4200292058 cites W2117692326 @default.
- W4200292058 cites W2130350952 @default.
- W4200292058 cites W2145025818 @default.
- W4200292058 cites W2146512944 @default.
- W4200292058 cites W2152793262 @default.
- W4200292058 cites W2157825442 @default.
- W4200292058 cites W2159707944 @default.
- W4200292058 cites W2164942973 @default.
- W4200292058 cites W2169895148 @default.
- W4200292058 cites W2172969444 @default.
- W4200292058 cites W2179438025 @default.
- W4200292058 cites W2234115940 @default.
- W4200292058 cites W2332292689 @default.
- W4200292058 cites W2336715782 @default.
- W4200292058 cites W2521132227 @default.
- W4200292058 cites W2553930786 @default.
- W4200292058 cites W2568414132 @default.
- W4200292058 cites W2575543752 @default.
- W4200292058 cites W2593540640 @default.
- W4200292058 cites W2615250694 @default.
- W4200292058 cites W2746926690 @default.
- W4200292058 cites W2760946358 @default.
- W4200292058 cites W2769383666 @default.
- W4200292058 cites W2772246530 @default.
- W4200292058 cites W2772723798 @default.
- W4200292058 cites W2787816121 @default.
- W4200292058 cites W2788633781 @default.
- W4200292058 cites W2794480084 @default.
- W4200292058 cites W2802742312 @default.
- W4200292058 cites W2885196877 @default.
- W4200292058 cites W2889176487 @default.
- W4200292058 cites W2949177718 @default.
- W4200292058 cites W2954610188 @default.
- W4200292058 cites W2969593942 @default.
- W4200292058 cites W3028304854 @default.
- W4200292058 cites W4213229297 @default.
- W4200292058 cites W4229719081 @default.
- W4200292058 cites W4233763100 @default.
- W4200292058 cites W4253861989 @default.
- W4200292058 doi "https://doi.org/10.1126/sciadv.abh2724" @default.
- W4200292058 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34936449" @default.
- W4200292058 hasPublicationYear "2021" @default.
- W4200292058 type Work @default.
- W4200292058 citedByCount "26" @default.
- W4200292058 countsByYear W42002920582022 @default.
- W4200292058 countsByYear W42002920582023 @default.
- W4200292058 crossrefType "journal-article" @default.
- W4200292058 hasAuthorship W4200292058A5001078878 @default.
- W4200292058 hasAuthorship W4200292058A5001605572 @default.
- W4200292058 hasAuthorship W4200292058A5001839093 @default.
- W4200292058 hasAuthorship W4200292058A5004442354 @default.
- W4200292058 hasAuthorship W4200292058A5004905094 @default.
- W4200292058 hasAuthorship W4200292058A5014224289 @default.