Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

126 related articles for article (PubMed ID: 36191612)

  • 1. Plasma lipid-based machine learning models provides a potential diagnostic tool for colorectal cancer patients.
    Yang C; Zhou S; Zhu J; Sheng H; Mao W; Fu Z; Chen Z
    Clin Chim Acta; 2022 Nov; 536():191-199. PubMed ID: 36191612
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Staging of colorectal cancer using lipid biomarkers and machine learning.
    Krishnan ST; Winkler D; Creek D; Anderson D; Kirana C; Maddern GJ; Fenix K; Hauben E; Rudd D; Voelcker NH
    Metabolomics; 2023 Sep; 19(10):84. PubMed ID: 37731020
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Lipid profiling in malignant mesothelioma reveals promising signatures for diagnosis and prognosis: A plasma-based LC-MS lipidomics study.
    Chen Z; Song S; Yang C; Dai Z; Gao Y; Li N; Zhu J; Mao W; Liu J
    Clin Chim Acta; 2022 Jan; 524():34-42. PubMed ID: 34843704
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Lipidomics random forest algorithm of seminal plasma is a promising method for enhancing the diagnosis of necrozoospermia.
    Deng T; Wang W; Fu Z; Xie Y; Zhou Y; Pu J; Chen K; Yao B; Li X; Yao J
    Metabolomics; 2024 May; 20(3):57. PubMed ID: 38773045
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Serum Untargeted UHPLC-HRMS-Based Lipidomics to Discover the Potential Biomarker of Colorectal Advanced Adenoma.
    Zhu Y; Wang L; Nong Y; Liang Y; Huang Z; Zhu P; Zhang Q
    Cancer Manag Res; 2021; 13():8865-8878. PubMed ID: 34858060
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data.
    Li H; Lin J; Xiao Y; Zheng W; Zhao L; Yang X; Zhong M; Liu H
    Technol Cancer Res Treat; 2021; 20():15330338211058352. PubMed ID: 34806496
    [No Abstract]   [Full Text] [Related]  

  • 7. Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system.
    Zhang Z; Huang L; Li J; Wang P
    BMC Bioinformatics; 2022 Apr; 23(1):124. PubMed ID: 35395711
    [TBL] [Abstract][Full Text] [Related]  

  • 8. UHPLC-HRMS-based serum untargeted lipidomics: Phosphatidylcholines and sphingomyelins are the main disturbed lipid markers to distinguish colorectal advanced adenoma from cancer.
    Chen H; Zhou H; Liang Y; Huang Z; Yang S; Wang X; She Z; Wei Z; Zhang Q
    J Pharm Biomed Anal; 2023 Sep; 234():115582. PubMed ID: 37473505
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A plasma lipidomics strategy reveals perturbed lipid metabolic pathways and potential lipid biomarkers of human colorectal cancer.
    Shen S; Yang L; Li L; Bai Y; Cai C; Liu H
    J Chromatogr B Analyt Technol Biomed Life Sci; 2017 Nov; 1068-1069():41-48. PubMed ID: 29028617
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Serum untargeted lipidomics by UHPLC-ESI-HRMS aids the biomarker discovery of colorectal adenoma.
    Zhou H; Nong Y; Zhu Y; Liang Y; Zhang J; Chen H; Zhu P; Zhang Q
    BMC Cancer; 2022 Mar; 22(1):314. PubMed ID: 35331175
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Metabolic detection of malignant brain gliomas through plasma lipidomic analysis and support vector machine-based machine learning.
    Zhou J; Ji N; Wang G; Zhang Y; Song H; Yuan Y; Yang C; Jin Y; Zhang Z; Zhang L; Yin Y
    EBioMedicine; 2022 Jul; 81():104097. PubMed ID: 35687958
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A conjunctive lipidomic approach reveals plasma ethanolamine plasmalogens and fatty acids as early diagnostic biomarkers for colorectal cancer patients.
    Liu T; Tan Z; Yu J; Peng F; Guo J; Meng W; Chen Y; Rao T; Liu Z; Peng J
    Expert Rev Proteomics; 2020 Mar; 17(3):233-242. PubMed ID: 32306783
    [No Abstract]   [Full Text] [Related]  

  • 13. Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome.
    Huang X; He Q; Hu H; Shi H; Zhang X; Xu Y
    Front Endocrinol (Lausanne); 2024; 15():1335269. PubMed ID: 38559697
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Identification of three potential novel biomarkers for early diagnosis of acute ischemic stroke via plasma lipidomics.
    Yu Y; Wen X; Lin JG; Liu J; Liang HF; Lin SW; Xu QG; Li JC
    Metabolomics; 2023 Mar; 19(4):32. PubMed ID: 36997715
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Identification of potential lipid biomarkers for active pulmonary tuberculosis using ultra-high-performance liquid chromatography-tandem mass spectrometry.
    Han YS; Chen JX; Li ZB; Chen J; Yi WJ; Huang H; Wei LL; Jiang TT; Li JC
    Exp Biol Med (Maywood); 2021 Feb; 246(4):387-399. PubMed ID: 33175608
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Application and Clinical Value of Machine Learning-Based Cervical Cancer Diagnosis and Prediction Model in Adjuvant Chemotherapy for Cervical Cancer: A Single-Center, Controlled, Non-Arbitrary Size Case-Control Study.
    Wang Y; Shen L; Jin J; Wang G
    Contrast Media Mol Imaging; 2022; 2022():2432291. PubMed ID: 35821886
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis.
    Kiritani S; Yoshimura K; Arita J; Kokudo T; Hakoda H; Tanimoto M; Ishizawa T; Akamatsu N; Kaneko J; Takeda S; Hasegawa K
    BMC Cancer; 2021 Mar; 21(1):262. PubMed ID: 33691644
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Preoperative prediction of regional lymph node metastasis of colorectal cancer based on
    He J; Wang Q; Zhang Y; Wu H; Zhou Y; Zhao S
    Ann Nucl Med; 2021 May; 35(5):617-627. PubMed ID: 33738763
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Identification of potential biomarkers with colorectal cancer based on bioinformatics analysis and machine learning.
    Hammad A; Elshaer M; Tang X
    Math Biosci Eng; 2021 Oct; 18(6):8997-9015. PubMed ID: 34814332
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Plasma-metabolite-based machine learning is a promising diagnostic approach for esophageal squamous cell carcinoma investigation.
    Chen Z; Huang X; Gao Y; Zeng S; Mao W
    J Pharm Anal; 2021 Aug; 11(4):505-514. PubMed ID: 34513127
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.