BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

300 related articles for article (PubMed ID: 29328377)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. Bioinformatics analysis of the CDK2 functions in neuroblastoma.
    Bo L; Wei B; Wang Z; Kong D; Gao Z; Miao Z
    Mol Med Rep; 2018 Mar; 17(3):3951-3959. PubMed ID: 29328425
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A support vector machine classifier for the prediction of osteosarcoma metastasis with high accuracy.
    He Y; Ma J; Ye X
    Int J Mol Med; 2017 Nov; 40(5):1357-1364. PubMed ID: 28901446
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Support vector machine classifier for prediction of the metastasis of colorectal cancer.
    Zhi J; Sun J; Wang Z; Ding W
    Int J Mol Med; 2018 Mar; 41(3):1419-1426. PubMed ID: 29328363
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A 80-gene set potentially predicts the relapse in laryngeal carcinoma optimized by support vector machine.
    Yang B; Guo Q; Wang F; Cai K; Bao X; Chu J
    Cancer Biomark; 2017; 19(1):65-73. PubMed ID: 28269752
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Signature microRNAs and long noncoding RNAs in laryngeal cancer recurrence identified using a competing endogenous RNA network.
    Tang Z; Wei G; Zhang L; Xu Z
    Mol Med Rep; 2019 Jun; 19(6):4806-4818. PubMed ID: 31059106
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. A 16-gene expression signature to distinguish stage I from stage II lung squamous carcinoma.
    Wang R; Cai Y; Zhang B; Wu Z
    Int J Mol Med; 2018 Mar; 41(3):1377-1384. PubMed ID: 29286069
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Identification of key candidate genes involved in melanoma metastasis.
    Chen J; Wu F; Shi Y; Yang D; Xu M; Lai Y; Liu Y
    Mol Med Rep; 2019 Aug; 20(2):903-914. PubMed ID: 31173190
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock.
    Long G; Yang C
    Mol Med Rep; 2020 Mar; 21(3):1561-1571. PubMed ID: 32016447
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Screening of characteristic genes in ulcerative colitis by integrating gene expression profiles.
    Han Y; Liu X; Dong H; Wen D
    BMC Gastroenterol; 2021 Oct; 21(1):415. PubMed ID: 34717557
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. Identifying the key genes and microRNAs in colorectal cancer liver metastasis by bioinformatics analysis and in vitro experiments.
    Zhang T; Guo J; Gu J; Wang Z; Wang G; Li H; Wang J
    Oncol Rep; 2019 Jan; 41(1):279-291. PubMed ID: 30542696
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 15.