BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

414 related articles for article (PubMed ID: 33725011)

  • 1. Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach.
    Ye GH; Alim M; Guan P; Huang DS; Zhou BS; Wu W
    PLoS One; 2021; 16(3):e0248597. PubMed ID: 33725011
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study.
    Wang YW; Shen ZZ; Jiang Y
    BMJ Open; 2019 Jun; 9(6):e025773. PubMed ID: 31209084
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model.
    Lv CX; An SY; Qiao BJ; Wu W
    BMC Infect Dis; 2021 Aug; 21(1):839. PubMed ID: 34412581
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China.
    Sun L; Zou LX
    Epidemiol Infect; 2018 Oct; 146(13):1680-1688. PubMed ID: 30078384
    [TBL] [Abstract][Full Text] [Related]  

  • 5. [Application of nonlinear autoregressive neural network in predicting incidence tendency of hemorrhagic fever with renal syndrome].
    Wu W; An S; Guo J; Guan P; Ren Y; Xia L; Zhou B
    Zhonghua Liu Xing Bing Xue Za Zhi; 2015 Dec; 36(12):1394-6. PubMed ID: 26850398
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Using an Autoregressive Integrated Moving Average Model to Predict the Incidence of Hemorrhagic Fever with Renal Syndrome in Zibo, China, 2004-2014.
    Wang T; Zhou Y; Wang L; Huang Z; Cui F; Zhai S
    Jpn J Infect Dis; 2016 Jul; 69(4):279-84. PubMed ID: 26370428
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China.
    Wu W; Guo J; An S; Guan P; Ren Y; Xia L; Zhou B
    PLoS One; 2015; 10(8):e0135492. PubMed ID: 26270814
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Epidemiological analysis of hemorrhagic fever with renal syndrome in China with the seasonal-trend decomposition method and the exponential smoothing model.
    Ke G; Hu Y; Huang X; Peng X; Lei M; Huang C; Gu L; Xian P; Yang D
    Sci Rep; 2016 Dec; 6():39350. PubMed ID: 27976704
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model.
    Liu Q; Liu X; Jiang B; Yang W
    BMC Infect Dis; 2011 Aug; 11():218. PubMed ID: 21838933
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks.
    Wu W; An SY; Guan P; Huang DS; Zhou BS
    BMC Infect Dis; 2019 May; 19(1):414. PubMed ID: 31088391
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Epidemiological characteristics and prediction model construction of hemorrhagic fever with renal syndrome in Quzhou City, China, 2005-2022.
    Gao Q; Wang S; Wang Q; Cao G; Fang C; Zhan B
    Front Public Health; 2023; 11():1333178. PubMed ID: 38274546
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome.
    Li Q; Guo NN; Han ZY; Zhang YB; Qi SX; Xu YG; Wei YM; Han X; Liu YY
    Am J Trop Med Hyg; 2012 Aug; 87(2):364-70. PubMed ID: 22855772
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models.
    Adeyinka DA; Muhajarine N
    BMC Med Res Methodol; 2020 Dec; 20(1):292. PubMed ID: 33267817
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis.
    Zhai M; Li W; Tie P; Wang X; Xie T; Ren H; Zhang Z; Song W; Quan D; Li M; Chen L; Qiu L
    BMC Infect Dis; 2021 Mar; 21(1):280. PubMed ID: 33740904
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The research on TBATS and ELM models for prediction of human brucellosis cases in mainland China: a time series study.
    Zhao D; Zhang H
    BMC Infect Dis; 2022 Dec; 22(1):934. PubMed ID: 36510150
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparison of simulation and predictive efficacy for hemorrhagic fever with renal syndrome incidence in mainland China based on five time series models.
    Wang Z; Yang C; Li B; Wu H; Xu Z; Feng Z
    Front Public Health; 2024; 12():1365942. PubMed ID: 38496387
    [TBL] [Abstract][Full Text] [Related]  

  • 17. ARIMA and ARIMA-ERNN models for prediction of pertussis incidence in mainland China from 2004 to 2021.
    Wang M; Pan J; Li X; Li M; Liu Z; Zhao Q; Luo L; Chen H; Chen S; Jiang F; Zhang L; Wang W; Wang Y
    BMC Public Health; 2022 Jul; 22(1):1447. PubMed ID: 35906580
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study.
    Alim M; Ye GH; Guan P; Huang DS; Zhou BS; Wu W
    BMJ Open; 2020 Dec; 10(12):e039676. PubMed ID: 33293308
    [TBL] [Abstract][Full Text] [Related]  

  • 19. SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA.
    Qi C; Zhang D; Zhu Y; Liu L; Li C; Wang Z; Li X
    BMC Med Res Methodol; 2020 Sep; 20(1):243. PubMed ID: 32993517
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Prevalence of hemorrhagic fever with renal syndrome in Yiyuan County, China, 2005-2014.
    Wang T; Liu J; Zhou Y; Cui F; Huang Z; Wang L; Zhai S
    BMC Infect Dis; 2016 Feb; 16():69. PubMed ID: 26852019
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 21.