BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

167 related articles for article (PubMed ID: 34075198)

  • 1. Predictive analysis of the number of human brucellosis cases in Xinjiang, China.
    Zheng Y; Zhang L; Wang C; Wang K; Guo G; Zhang X; Wang J
    Sci Rep; 2021 Jun; 11(1):11513. PubMed ID: 34075198
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Epidemiology and time series analysis of human brucellosis in Tebessa province, Algeria, from 2000 to 2020.
    Akermi SE; L'Hadj M; Selmane S
    J Res Health Sci; 2022 Mar; 22(1):e00544. PubMed ID: 36511254
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model.
    Wang Y; Xu C; Zhang S; Wang Z; Yang L; Zhu Y; Yuan J
    BMJ Open; 2019 Jul; 9(7):e024409. PubMed ID: 31371283
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. A Hybrid Approach Based on Seasonal Autoregressive Integrated Moving Average and Neural Network Autoregressive Models to Predict Scorpion Sting Incidence in El Oued Province, Algeria, From 2005 to 2020.
    Zenia S; L'Hadj M; Selmane S
    J Res Health Sci; 2023 Sep; 23(3):e00586. PubMed ID: 38315901
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Time series model for forecasting the number of new admission inpatients.
    Zhou L; Zhao P; Wu D; Cheng C; Huang H
    BMC Med Inform Decis Mak; 2018 Jun; 18(1):39. PubMed ID: 29907102
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Time-series analysis on human brucellosis during 2004-2013 in Shandong Province, China.
    Yang L; Bi ZW; Kou ZQ; Li XJ; Zhang M; Wang M; Zhang LY; Zhao ZT
    Zoonoses Public Health; 2015 May; 62(3):228-35. PubMed ID: 25043064
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Influence and prediction of meteorological factors on brucellosis in a northwest region of China.
    Zheng H; Liu D; Zhao X; Zhao X; Liu Y; Li D; Shi T; Ren X
    Environ Sci Pollut Res Int; 2023 Jan; 30(4):9962-9973. PubMed ID: 36064850
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models.
    Chen Q; Zhao H; Qiu H; Wang Q; Zeng D; Ye M
    J Infect Dev Ctries; 2022 Aug; 16(8):1343-1350. PubMed ID: 36099379
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of reported monthly incidence of hepatitis B in Hainan Province of China based on SARIMA-BPNN model.
    Fang K; Cao L; Fu Z; Li W
    Medicine (Baltimore); 2023 Oct; 102(41):e35054. PubMed ID: 37832091
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A prediction method of fire frequency: Based on the optimization of SARIMA model.
    Ma S; Liu Q; Zhang Y
    PLoS One; 2021; 16(8):e0255857. PubMed ID: 34370785
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China.
    Yu L; Zhou L; Tan L; Jiang H; Wang Y; Wei S; Nie S
    PLoS One; 2014; 9(6):e98241. PubMed ID: 24893000
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model.
    Yu G; Feng H; Feng S; Zhao J; Xu J
    PLoS One; 2021; 16(2):e0246673. PubMed ID: 33544752
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Application of a combined model with seasonal autoregressive integrated moving average and support vector regression in forecasting hand-foot-mouth disease incidence in Wuhan, China.
    Zou JJ; Jiang GF; Xie XX; Huang J; Yang XB
    Medicine (Baltimore); 2019 Feb; 98(6):e14195. PubMed ID: 30732135
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Time-series analysis of tuberculosis from 2005 to 2017 in China.
    Wang H; Tian CW; Wang WM; Luo XM
    Epidemiol Infect; 2018 Jun; 146(8):935-939. PubMed ID: 29708082
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China.
    Zhao Z; Zhai M; Li G; Gao X; Song W; Wang X; Ren H; Cui Y; Qiao Y; Ren J; Chen L; Qiu L
    BMC Infect Dis; 2023 Feb; 23(1):71. PubMed ID: 36747126
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.