BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

254 related articles for article (PubMed ID: 34806496)

  • 1. 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]  

  • 2. Blood Biomarkers Panels for Screening of Colorectal Cancer and Adenoma on a Machine Learning-Assisted Detection Platform.
    Wang H; Zhou Z; Li H; Xiang W; Lan Y; Dou X; Zhang X
    Cancer Control; 2023; 30():10732748231222109. PubMed ID: 38146088
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Altered Serum Annexin A2 Might Be a New Potential Diagnostic Biomarker in Human Colorectal Cancer.
    Hu D; Shen B; Yu M; Zha X; Zhou Y; Chen F; Ren J; Zhang L
    Ann Clin Lab Sci; 2020 Nov; 50(6):726-733. PubMed ID: 33334786
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Utility of machine learning in developing a predictive model for early-age-onset colorectal neoplasia using electronic health records.
    Hussan H; Zhao J; Badu-Tawiah AK; Stanich P; Tabung F; Gray D; Ma Q; Kalady M; Clinton SK
    PLoS One; 2022; 17(3):e0265209. PubMed ID: 35271664
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A reliable method for colorectal cancer prediction based on feature selection and support vector machine.
    Zhao D; Liu H; Zheng Y; He Y; Lu D; Lyu C
    Med Biol Eng Comput; 2019 Apr; 57(4):901-912. PubMed ID: 30478811
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease.
    Wang C; Chen X; Du L; Zhan Q; Yang T; Fang Z
    Comput Methods Programs Biomed; 2020 May; 188():105267. PubMed ID: 31841787
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Circulating fibrinogen to pre-albumin ratio is a promising biomarker for diagnosis of colorectal cancer.
    Sun F; Tan YA; Gao QF; Li SQ; Zhang J; Chen QG; Jiang YH; Zhang L; Ying HQ; Wang XZ
    J Clin Lab Anal; 2019 Jan; 33(1):e22635. PubMed ID: 30047185
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Diagnostic Value of Carcinoembryonic Antigen in Ascites for Colorectal Cancer with Peritoneal Carcinomatosis.
    Song SE; Choi P; Kim JH; Jung K; Kim SE; Moon W; Park MI; Park SJ
    Korean J Gastroenterol; 2018 Jun; 71(6):332-337. PubMed ID: 29943560
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Exploration of the application potential of serum multi-biomarker model in colorectal cancer screening.
    Xu R; Shen J; Song Y; Lu J; Liu Y; Cao Y; Wang Z; Zhang J
    Sci Rep; 2024 May; 14(1):10127. PubMed ID: 38698075
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model.
    Bozorgmehr A; Thielmann A; Weltermann B
    PLoS One; 2021; 16(5):e0250842. PubMed ID: 33945572
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Improved diagnosis of colorectal cancer using combined biomarkers including Fusobacterium nucleatum, fecal occult blood, transferrin, CEA, CA19-9, gender, and age.
    Zhao R; Xia D; Chen Y; Kai Z; Ruan F; Xia C; Gong J; Wu J; Wang X
    Cancer Med; 2023 Jul; 12(13):14636-14645. PubMed ID: 37162269
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Development and validation of a predictive model for detection of colorectal cancer in primary care by analysis of complete blood counts: a binational retrospective study.
    Kinar Y; Kalkstein N; Akiva P; Levin B; Half EE; Goldshtein I; Chodick G; Shalev V
    J Am Med Inform Assoc; 2016 Sep; 23(5):879-90. PubMed ID: 26911814
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Using gut microbiota as a diagnostic tool for colorectal cancer: machine learning techniques reveal promising results.
    Lu F; Lei T; Zhou J; Liang H; Cui P; Zuo T; Ye L; Chen H; Huang J
    J Med Microbiol; 2023 Jun; 72(6):. PubMed ID: 37288545
    [No Abstract]   [Full Text] [Related]  

  • 14. Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis.
    Zhang B; Liang XL; Gao HY; Ye LS; Wang YG
    Genet Mol Res; 2016 May; 15(2):. PubMed ID: 27323037
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study.
    Achilonu OJ; Fabian J; Bebington B; Singh E; Eijkemans MJC; Musenge E
    Front Public Health; 2021; 9():694306. PubMed ID: 34307286
    [No Abstract]   [Full Text] [Related]  

  • 16. Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records.
    Zapata RD; Huang S; Morris E; Wang C; Harle C; Magoc T; Mardini M; Loftus T; Modave F
    PLoS One; 2023; 18(10):e0292888. PubMed ID: 37862334
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Development and validation of machine learning models for postoperative venous thromboembolism prediction in colorectal cancer inpatients: a retrospective study.
    Qin L; Liang Z; Xie J; Ye G; Guan P; Huang Y; Li X
    J Gastrointest Oncol; 2023 Feb; 14(1):220-232. PubMed ID: 36915444
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predictive value of monocyte to high-density lipoprotein cholesterol ratio and tumor markers in colorectal cancer and their relationship with clinicopathological characteristics.
    Zhang X; Qin H; Tan X; Mo Y; Li Z; Huang G; Wei Z
    World J Surg Oncol; 2023 Jul; 21(1):200. PubMed ID: 37420210
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A machine learning-based framework to identify type 2 diabetes through electronic health records.
    Zheng T; Xie W; Xu L; He X; Zhang Y; You M; Yang G; Chen Y
    Int J Med Inform; 2017 Jan; 97():120-127. PubMed ID: 27919371
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.