BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

240 related articles for article (PubMed ID: 32705275)

  • 1. Risk gene identification and support vector machine learning to construct an early diagnosis model of myocardial infarction.
    Fang HZ; Hu DL; Li Q; Tu S
    Mol Med Rep; 2020 Sep; 22(3):1775-1782. PubMed ID: 32705275
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Identification of risk genes associated with myocardial infarction based on the recursive feature elimination algorithm and support vector machine classifier.
    Yang X
    Mol Med Rep; 2018 Jan; 17(1):1555-1560. PubMed ID: 29138828
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Identification of feature autophagy-related genes in patients with acute myocardial infarction based on bioinformatics analyses.
    Du Y; Zhao E; Zhang Y
    Biosci Rep; 2020 Jul; 40(7):. PubMed ID: 32597946
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Construction of a 26‑feature gene support vector machine classifier for smoking and non‑smoking lung adenocarcinoma sample classification.
    Yang L; Sun L; Wang W; Xu H; Li Y; Zhao JY; Liu DZ; Wang F; Zhang LY
    Mol Med Rep; 2018 Feb; 17(2):3005-3013. PubMed ID: 29257283
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A 21‑gene Support Vector Machine classifier and a 10‑gene risk score system constructed for patients with gastric cancer.
    Jiang H; Gu J; Du J; Qi X; Qian C; Fei B
    Mol Med Rep; 2020 Jan; 21(1):347-359. PubMed ID: 31939629
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Identification of hub biomarkers of myocardial infarction by single-cell sequencing, bioinformatics, and machine learning.
    Zhang Q; Guo Y; Zhang B; Liu H; Peng Y; Wang D; Zhang D
    Front Cardiovasc Med; 2022; 9():939972. PubMed ID: 35958412
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Establishment of a SVM classifier to predict recurrence of ovarian cancer.
    Zhou J; Li L; Wang L; Li X; Xing H; Cheng L
    Mol Med Rep; 2018 Oct; 18(4):3589-3598. PubMed ID: 30106117
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A multigene support vector machine predictor for metastasis of cutaneous melanoma.
    Wei D
    Mol Med Rep; 2018 Feb; 17(2):2907-2914. PubMed ID: 29257259
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identification of Featured Metabolism-Related Genes in Patients with Acute Myocardial Infarction.
    Xie H; Zha E; Zhang Y
    Dis Markers; 2020; 2020():8880004. PubMed ID: 33354250
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A machine learning-based diagnostic model for myocardial infarction patients: Analysis of neutrophil extracellular traps-related genes and eQTL Mendelian randomization.
    Sheng M; Cui X
    Medicine (Baltimore); 2024 Mar; 103(12):e37363. PubMed ID: 38518057
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Potential role of a three-gene signature in predicting diagnosis in patients with myocardial infarction.
    Yao Y; Zhao J; Zhou X; Hu J; Wang Y
    Bioengineered; 2021 Dec; 12(1):2734-2749. PubMed ID: 34130601
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.
    Li S; Chen B; Chen H; Hua Z; Shao Y; Yin H; Wang J
    PLoS One; 2021; 16(9):e0257343. PubMed ID: 34555052
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods.
    Tuo Y; An N; Zhang M
    Mol Med Rep; 2018 Mar; 17(3):4281-4290. PubMed ID: 29328377
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Gene expression profiles reveal key genes for early diagnosis and treatment of adamantinomatous craniopharyngioma.
    Yang J; Hou Z; Wang C; Wang H; Zhang H
    Cancer Gene Ther; 2018 Oct; 25(9-10):227-239. PubMed ID: 29681617
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Identification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysis.
    Wen P; Chidanguro T; Shi Z; Gu H; Wang N; Wang T; Li Y; Gao J
    Mol Med Rep; 2018 Aug; 18(2):1538-1550. PubMed ID: 29845250
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Construction of a 5-feature gene model by support vector machine for classifying osteoporosis samples.
    Hu M; Zou L; Lu J; Yang Z; Chen Y; Xu Y; Sun C
    Bioengineered; 2021 Dec; 12(1):6821-6830. PubMed ID: 34622712
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Coronary artery disease associated specific modules and feature genes revealed by integrative methods of WGCNA, MetaDE and machine learning.
    Wang Y; Liu T; Liu Y; Chen J; Xin B; Wu M; Cui W
    Gene; 2019 Aug; 710():122-130. PubMed ID: 31075415
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Identification of feature risk pathways of smoking-induced lung cancer based on SVM.
    Chen R; Lin J
    PLoS One; 2020; 15(6):e0233445. PubMed ID: 32497048
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Identification of key genes involved in myocardial infarction.
    Qiu L; Liu X
    Eur J Med Res; 2019 Jul; 24(1):22. PubMed ID: 31269974
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Identification of long non-coding RNAs biomarkers for early diagnosis of myocardial infarction from the dysregulated coding-non-coding co-expression network.
    Sun C; Jiang H; Sun Z; Gui Y; Xia H
    Oncotarget; 2016 Nov; 7(45):73541-73551. PubMed ID: 27634901
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.