These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

140 related articles for article (PubMed ID: 36931444)

  • 1. Characteristics prediction of hydrothermal biochar using data enhanced interpretable machine learning.
    Chen C; Wang Z; Ge Y; Liang R; Hou D; Tao J; Yan B; Zheng W; Velichkova R; Chen G
    Bioresour Technol; 2023 Jun; 377():128893. PubMed ID: 36931444
    [TBL] [Abstract][Full Text] [Related]  

  • 2. The application of machine learning methods for prediction of metal immobilization remediation by biochar amendment in soil.
    Sun Y; Zhang Y; Lu L; Wu Y; Zhang Y; Kamran MA; Chen B
    Sci Total Environ; 2022 Jul; 829():154668. PubMed ID: 35318058
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass.
    Li Y; Gupta R; You S
    Bioresour Technol; 2022 Sep; 359():127511. PubMed ID: 35752259
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Multi-output neural network model for predicting biochar yield and composition.
    Wang Y; Xu L; Li J; Ren Z; Liu W; Ai Y; Zhou Y; Li Q; Zhang B; Guo N; Qu J; Zhang Y
    Sci Total Environ; 2024 Oct; 945():173942. PubMed ID: 38880151
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning.
    Palansooriya KN; Li J; Dissanayake PD; Suvarna M; Li L; Yuan X; Sarkar B; Tsang DCW; Rinklebe J; Wang X; Ok YS
    Environ Sci Technol; 2022 Apr; 56(7):4187-4198. PubMed ID: 35289167
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions.
    Zhu X; Li Y; Wang X
    Bioresour Technol; 2019 Sep; 288():121527. PubMed ID: 31136889
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Machine learning models for predicting biochar properties from lignocellulosic biomass torrefaction.
    Su G; Jiang P
    Bioresour Technol; 2024 May; 399():130519. PubMed ID: 38437964
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Machine learning assisted predicting and engineering specific surface area and total pore volume of biochar.
    Li H; Ai Z; Yang L; Zhang W; Yang Z; Peng H; Leng L
    Bioresour Technol; 2023 Feb; 369():128417. PubMed ID: 36462763
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms.
    Khan M; Ullah Z; MaĊĦek O; Raza Naqvi S; Nouman Aslam Khan M
    Bioresour Technol; 2022 Jul; 355():127215. PubMed ID: 35470005
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Enhancing lead adsorption capacity prediction in biochar: a comparative study of machine learning models and parameter optimization.
    Liang J; Wu M; Hu Z; Zhao M; Xue Y
    Environ Sci Pollut Res Int; 2023 Dec; 30(57):120832-120843. PubMed ID: 37945960
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Machine learning prediction of biochar yield based on biomass characteristics.
    Ma J; Zhang S; Liu X; Wang J
    Bioresour Technol; 2023 Dec; 389():129820. PubMed ID: 37805089
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models.
    Cipullo S; Snapir B; Prpich G; Campo P; Coulon F
    Chemosphere; 2019 Jan; 215():388-395. PubMed ID: 30347356
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting crop root concentration factors of organic contaminants with machine learning models.
    Gao F; Shen Y; Brett Sallach J; Li H; Zhang W; Li Y; Liu C
    J Hazard Mater; 2022 Feb; 424(Pt B):127437. PubMed ID: 34678561
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning in the evaluation and prediction models of biochar application: A review.
    Chen MW; Chang MS; Mao Y; Hu S; Kung CC
    Sci Prog; 2023; 106(1):368504221148842. PubMed ID: 36628421
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models.
    Ke B; Nguyen H; Bui XN; Bui HB; Choi Y; Zhou J; Moayedi H; Costache R; Nguyen-Trang T
    Chemosphere; 2021 Aug; 276():130204. PubMed ID: 34088091
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Thermogravimetric experiments based prediction of biomass pyrolysis behavior: A comparison of typical machine learning regression models in Scikit-learn.
    Zhong Y; Liu F; Huang G; Zhang J; Li C; Ding Y
    Mar Pollut Bull; 2024 May; 202():116361. PubMed ID: 38636345
    [TBL] [Abstract][Full Text] [Related]  

  • 17. The application of machine learning methods for prediction of metal sorption onto biochars.
    Zhu X; Wang X; Ok YS
    J Hazard Mater; 2019 Oct; 378():120727. PubMed ID: 31202073
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Review on sustainable production of biochar through hydrothermal liquefaction: Physico-chemical properties and applications.
    Ponnusamy VK; Nagappan S; Bhosale RR; Lay CH; Duc Nguyen D; Pugazhendhi A; Chang SW; Kumar G
    Bioresour Technol; 2020 Aug; 310():123414. PubMed ID: 32354676
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Biochar from constructed wetland biomass waste: A review of its potential and challenges.
    Cui X; Wang J; Wang X; Khan MB; Lu M; Khan KY; Song Y; He Z; Yang X; Yan B; Chen G
    Chemosphere; 2022 Jan; 287(Pt 3):132259. PubMed ID: 34543904
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability.
    Charlton CE; Poon MTC; Brennan PM; Fleuriot JD
    Comput Methods Programs Biomed; 2023 May; 233():107482. PubMed ID: 36947980
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.