BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

147 related articles for article (PubMed ID: 20952233)

  • 21. A tentative quantitative structure-toxicity relationship study of benzodiazepine drugs.
    Funar-Timofei S; Ionescu D; Suzuki T
    Toxicol In Vitro; 2010 Feb; 24(1):184-200. PubMed ID: 19765642
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Classification models for HERG inhibitors by counter-propagation neural networks.
    Thai KM; Ecker GF
    Chem Biol Drug Des; 2008 Oct; 72(4):279-89. PubMed ID: 18844674
    [TBL] [Abstract][Full Text] [Related]  

  • 23. QSARs and activity predicting models for competitive inhibitors of adenosine deaminase.
    Sadat Hayatshahi SH; Abdolmaleki P; Ghiasi M; Safarian S
    FEBS Lett; 2007 Feb; 581(3):506-14. PubMed ID: 17250831
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Endocrine disruption profile analysis of 11,416 chemicals from chemometrical tools.
    Devillers J; Marchand-Geneste N; Doré JC; Porcher JM; Poroikov V
    SAR QSAR Environ Res; 2007; 18(3-4):181-93. PubMed ID: 17514564
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Comparative structure-toxicity relationship study of substituted benzenes to Tetrahymena pyriformis using shuffling-adaptive neuro fuzzy inference system and artificial neural networks.
    Jalali-Heravi M; Kyani A
    Chemosphere; 2008 Jun; 72(5):733-40. PubMed ID: 18499226
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Genetic neural networks for quantitative structure-activity relationships: improvements and application of benzodiazepine affinity for benzodiazepine/GABAA receptors.
    So SS; Karplus M
    J Med Chem; 1996 Dec; 39(26):5246-56. PubMed ID: 8978853
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous).
    Hemmateenejad B; Akhond M; Miri R; Shamsipur M
    J Chem Inf Comput Sci; 2003; 43(4):1328-34. PubMed ID: 12870926
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Prediction of cytotoxicity data (CC(50)) of anti-HIV 5-phenyl-1-phenylamino-1H-imidazole derivatives by artificial neural network trained with Levenberg-Marquardt algorithm.
    Arab Chamjangali M; Beglari M; Bagherian G
    J Mol Graph Model; 2007 Jul; 26(1):360-7. PubMed ID: 17350867
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Modeling the activity of furin inhibitors using artificial neural network.
    Worachartcheewan A; Nantasenamat C; Naenna T; Isarankura-Na-Ayudhya C; Prachayasittikul V
    Eur J Med Chem; 2009 Apr; 44(4):1664-73. PubMed ID: 18977558
    [TBL] [Abstract][Full Text] [Related]  

  • 30. QSAR modeling of anti-invasive activity of organic compounds using structural descriptors.
    Katritzky AR; Kuanar M; Dobchev DA; Vanhoecke BW; Karelson M; Parmar VS; Stevens CV; Bracke ME
    Bioorg Med Chem; 2006 Oct; 14(20):6933-9. PubMed ID: 16908166
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Toward an optimal procedure for PC-ANN model building: prediction of the carcinogenic activity of a large set of drugs.
    Hemmateenejad B; Safarpour MA; Miri R; Nesari N
    J Chem Inf Model; 2005; 45(1):190-9. PubMed ID: 15667145
    [TBL] [Abstract][Full Text] [Related]  

  • 32. A comparison of methods for modeling quantitative structure-activity relationships.
    Sutherland JJ; O'Brien LA; Weaver DF
    J Med Chem; 2004 Oct; 47(22):5541-54. PubMed ID: 15481990
    [TBL] [Abstract][Full Text] [Related]  

  • 33. QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants.
    Kovarich S; Papa E; Gramatica P
    J Hazard Mater; 2011 Jun; 190(1-3):106-12. PubMed ID: 21454014
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Performance of the flow cytometric E-screen assay in screening estrogenicity of pure compounds and environmental samples.
    Vanparys C; Depiereux S; Nadzialek S; Robbens J; Blust R; Kestemont P; De Coen W
    Sci Total Environ; 2010 Sep; 408(20):4451-60. PubMed ID: 20633926
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Prediction of fathead minnow acute toxicity of organic compounds from molecular structure.
    Eldred DV; Weikel CL; Jurs PC; Kaiser KL
    Chem Res Toxicol; 1999 Jul; 12(7):670-8. PubMed ID: 10409408
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Assessing the chemical-induced estrogenicity using in silico and in vitro methods.
    Goya-Jorge E; Amber M; Gozalbes R; Connolly L; Barigye SJ
    Environ Toxicol Pharmacol; 2021 Oct; 87():103688. PubMed ID: 34119701
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Neural networks.
    Yang ZR
    Methods Mol Biol; 2010; 609():197-222. PubMed ID: 20221921
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Studying the explanatory capacity of artificial neural networks for understanding environmental chemical quantitative structure-activity relationship models.
    Yang L; Wang P; Jiang Y; Chen J
    J Chem Inf Model; 2005; 45(6):1804-11. PubMed ID: 16309287
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Prediction of HPLC retention index using artificial neural networks and IGroup E-state indices.
    Albaugh DR; Hall LM; Hill DW; Kertesz TM; Parham M; Hall LH; Grant DF
    J Chem Inf Model; 2009 Apr; 49(4):788-99. PubMed ID: 19309176
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Modeling of cyclin-dependent kinase inhibition by 1H-pyrazolo[3,4-d]pyrimidine derivatives using artificial neural network ensembles.
    Fernández M; Tundidor-Camba A; Caballero J
    J Chem Inf Model; 2005; 45(6):1884-95. PubMed ID: 16309296
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 8.