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 *

156 related articles for article (PubMed ID: 35688391)

  • 1. Critical features identification for chemical chronic toxicity based on mechanistic forecast models.
    Wang X; Li F; Chen J; Teng Y; Ji C; Wu H
    Environ Pollut; 2022 Aug; 307():119584. PubMed ID: 35688391
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Environmental toxicity risk evaluation of nitroaromatic compounds: Machine learning driven binary/multiple classification and design of safe alternatives.
    Hao Y; Fan T; Sun G; Li F; Zhang N; Zhao L; Zhong R
    Food Chem Toxicol; 2022 Dec; 170():113461. PubMed ID: 36243219
    [TBL] [Abstract][Full Text] [Related]  

  • 3. QSAR modelling study of the bioconcentration factor and toxicity of organic compounds to aquatic organisms using machine learning and ensemble methods.
    Ai H; Wu X; Zhang L; Qi M; Zhao Y; Zhao Q; Zhao J; Liu H
    Ecotoxicol Environ Saf; 2019 Sep; 179():71-78. PubMed ID: 31026752
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds.
    He Y; Liu G; Hu S; Wang X; Jia J; Zhou H; Yan X
    J Hazard Mater; 2023 Sep; 458():131942. PubMed ID: 37390684
    [TBL] [Abstract][Full Text] [Related]  

  • 5.
    Liu L; Yang H; Cai Y; Cao Q; Sun L; Wang Z; Li W; Liu G; Lee PW; Tang Y
    Toxicol Res (Camb); 2019 May; 8(3):341-352. PubMed ID: 31160968
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Modeling and insights into the structural basis of chemical acute aquatic toxicity.
    Zhang R; Guo H; Hua Y; Cui X; Shi Y; Li X
    Ecotoxicol Environ Saf; 2022 Sep; 242():113940. PubMed ID: 35999760
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Safer and greener chemicals for the aquatic ecosystem: Chemometric modeling of the prolonged and chronic aquatic toxicity of chemicals on Oryzias latipes.
    Kumar A; Ojha PK; Roy K
    Aquat Toxicol; 2024 Jun; 273():106985. PubMed ID: 38875952
    [TBL] [Abstract][Full Text] [Related]  

  • 8. In silico estimation of chemical aquatic toxicity on crustaceans using chemical category methods.
    Cao Q; Liu L; Yang H; Cai Y; Li W; Liu G; Lee PW; Tang Y
    Environ Sci Process Impacts; 2018 Sep; 20(9):1234-1243. PubMed ID: 30069560
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Zebrafish AC
    Lavado GJ; Gadaleta D; Toma C; Golbamaki A; Toropov AA; Toropova AP; Marzo M; Baderna D; Arning J; Benfenati E
    Ecotoxicol Environ Saf; 2020 Oct; 202():110936. PubMed ID: 32800219
    [TBL] [Abstract][Full Text] [Related]  

  • 10. ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity.
    Lei T; Chen F; Liu H; Sun H; Kang Y; Li D; Li Y; Hou T
    Mol Pharm; 2017 Jul; 14(7):2407-2421. PubMed ID: 28595388
    [TBL] [Abstract][Full Text] [Related]  

  • 11. In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.
    Hua Y; Shi Y; Cui X; Li X
    Mol Divers; 2021 Aug; 25(3):1585-1596. PubMed ID: 34196933
    [TBL] [Abstract][Full Text] [Related]  

  • 12. An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors.
    Jaganathan K; Tayara H; Chong KT
    Pharmaceutics; 2022 Apr; 14(4):. PubMed ID: 35456666
    [TBL] [Abstract][Full Text] [Related]  

  • 13. ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.
    Lei T; Sun H; Kang Y; Zhu F; Liu H; Zhou W; Wang Z; Li D; Li Y; Hou T
    Mol Pharm; 2017 Nov; 14(11):3935-3953. PubMed ID: 29037046
    [TBL] [Abstract][Full Text] [Related]  

  • 14. [Quantitative structure-activity relationship model for prediction of cardiotoxicity of chemical components in traditional Chinese medicines].
    Beijing Da Xue Xue Bao Yi Xue Ban; 2017 Jun; 49(3):551-556. PubMed ID: 28628163
    [TBL] [Abstract][Full Text] [Related]  

  • 15. In silico prediction of chemical reproductive toxicity using machine learning.
    Jiang C; Yang H; Di P; Li W; Tang Y; Liu G
    J Appl Toxicol; 2019 Jun; 39(6):844-854. PubMed ID: 30687929
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine learning-based models to predict aquatic ecological risk for engineered nanoparticles: using hazard concentration for 5% of species as an endpoint.
    Qi Q; Wang Z
    Environ Sci Pollut Res Int; 2024 Apr; 31(17):25114-25128. PubMed ID: 38467999
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Insights into pesticide toxicity against aquatic organism: QSTR models on Daphnia Magna.
    He L; Xiao K; Zhou C; Li G; Yang H; Li Z; Cheng J
    Ecotoxicol Environ Saf; 2019 May; 173():285-292. PubMed ID: 30776561
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Predicting the hazardous dose of industrial chemicals in warm-blooded species using machine learning-based modelling approaches.
    Gupta S; Basant N; Singh KP
    SAR QSAR Environ Res; 2015 Jun; 26(6):479-98. PubMed ID: 26087353
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints.
    Feng H; Zhang L; Li S; Liu L; Yang T; Yang P; Zhao J; Arkin IT; Liu H
    Toxicol Lett; 2021 Apr; 340():4-14. PubMed ID: 33421549
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Application of cross-validation strategies to avoid overestimation of performance of 2D-QSAR models for the prediction of aquatic toxicity of chemical mixtures.
    Chatterjee M; Roy K
    SAR QSAR Environ Res; 2022 Jun; 33(6):463-484. PubMed ID: 35638563
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.