BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

213 related articles for article (PubMed ID: 29809005)

  • 1. Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models.
    Alves VM; Golbraikh A; Capuzzi SJ; Liu K; Lam WI; Korn DR; Pozefsky D; Andrade CH; Muratov EN; Tropsha A
    J Chem Inf Model; 2018 Jun; 58(6):1214-1223. PubMed ID: 29809005
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Development of a read-across-derived classification model for the predictions of mutagenicity data and its comparison with traditional QSAR models and expert systems.
    Pandey SK; Roy K
    Toxicology; 2023 Dec; 500():153676. PubMed ID: 37993082
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Improvement of quantitative structure-activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project.
    Honma M; Kitazawa A; Cayley A; Williams RV; Barber C; Hanser T; Saiakhov R; Chakravarti S; Myatt GJ; Cross KP; Benfenati E; Raitano G; Mekenyan O; Petkov P; Bossa C; Benigni R; Battistelli CL; Giuliani A; Tcheremenskaia O; DeMeo C; Norinder U; Koga H; Jose C; Jeliazkova N; Kochev N; Paskaleva V; Yang C; Daga PR; Clark RD; Rathman J
    Mutagenesis; 2019 Mar; 34(1):3-16. PubMed ID: 30357358
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Towards quantitative read across: Prediction of Ames mutagenicity in a large database.
    Benigni R
    Regul Toxicol Pharmacol; 2019 Nov; 108():104434. PubMed ID: 31374229
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Combinatorial QSAR of ambergris fragrance compounds.
    Kovatcheva A; Golbraikh A; Oloff S; Xiao YD; Zheng W; Wolschann P; Buchbauer G; Tropsha A
    J Chem Inf Comput Sci; 2004; 44(2):582-95. PubMed ID: 15032539
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals.
    Silva AC; Borba JVVB; Alves VM; Hall SUS; Furnham N; Kleinstreuer N; Muratov E; Tropsha A; Andrade CH
    Artif Intell Life Sci; 2021 Dec; 1():. PubMed ID: 35935266
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Chembench: A Publicly Accessible, Integrated Cheminformatics Portal.
    Capuzzi SJ; Kim IS; Lam WI; Thornton TE; Muratov EN; Pozefsky D; Tropsha A
    J Chem Inf Model; 2017 Feb; 57(2):105-108. PubMed ID: 28045544
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Three new consensus QSAR models for the prediction of Ames genotoxicity.
    Votano JR; Parham M; Hall LH; Kier LB; Oloff S; Tropsha A; Xie Q; Tong W
    Mutagenesis; 2004 Sep; 19(5):365-77. PubMed ID: 15388809
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Merging applicability domains for in silico assessment of chemical mutagenicity.
    Liu R; Wallqvist A
    J Chem Inf Model; 2014 Mar; 54(3):793-800. PubMed ID: 24494696
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A new approach to radial basis function approximation and its application to QSAR.
    Zakharov AV; Peach ML; Sitzmann M; Nicklaus MC
    J Chem Inf Model; 2014 Mar; 54(3):713-9. PubMed ID: 24451033
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities.
    Valencia A; Prous J; Mora O; Sadrieh N; Valerio LG
    Toxicol Appl Pharmacol; 2013 Dec; 273(3):427-34. PubMed ID: 24090816
    [TBL] [Abstract][Full Text] [Related]  

  • 12. ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data.
    Banerjee A; Roy K
    Environ Sci Process Impacts; 2024 Jun; 26(6):991-1007. PubMed ID: 38743054
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A practice of expert review by read-across using QSAR Toolbox.
    Fukuchi J; Kitazawa A; Hirabayashi K; Honma M
    Mutagenesis; 2019 Mar; 34(1):49-54. PubMed ID: 30690463
    [TBL] [Abstract][Full Text] [Related]  

  • 14. In Silico Study of In Vitro GPCR Assays by QSAR Modeling.
    Mansouri K; Judson RS
    Methods Mol Biol; 2016; 1425():361-81. PubMed ID: 27311474
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals.
    Braga RC; Alves VM; Muratov EN; Strickland J; Kleinstreuer N; Trospsha A; Andrade CH
    J Chem Inf Model; 2017 May; 57(5):1013-1017. PubMed ID: 28459556
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Descriptor Selection Improvements for Quantitative Structure-Activity Relationships.
    Xia LY; Wang QY; Cao Z; Liang Y
    Int J Neural Syst; 2019 Nov; 29(9):1950016. PubMed ID: 31390912
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Using a hybrid read-across method to evaluate chemical toxicity based on chemical structure and biological data.
    Guo Y; Zhao L; Zhang X; Zhu H
    Ecotoxicol Environ Saf; 2019 Aug; 178():178-187. PubMed ID: 31004930
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis.
    Zhu H; Tropsha A; Fourches D; Varnek A; Papa E; Gramatica P; Oberg T; Dao P; Cherkasov A; Tetko IV
    J Chem Inf Model; 2008 Apr; 48(4):766-84. PubMed ID: 18311912
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information.
    Shah I; Liu J; Judson RS; Thomas RS; Patlewicz G
    Regul Toxicol Pharmacol; 2016 Aug; 79():12-24. PubMed ID: 27174420
    [TBL] [Abstract][Full Text] [Related]  

  • 20. In Silico Prediction of Chemically Induced Mutagenicity: A Weight of Evidence Approach Integrating Information from QSAR Models and Read-Across Predictions.
    Mombelli E; Raitano G; Benfenati E
    Methods Mol Biol; 2022; 2425():149-183. PubMed ID: 35188632
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.