BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

166 related articles for article (PubMed ID: 16697155)

  • 1. Boosting support vector regression in QSAR studies of bioactivities of chemical compounds.
    Zhou YP; Jiang JH; Lin WQ; Zou HY; Wu HL; Shen GL; Yu RQ
    Eur J Pharm Sci; 2006 Jul; 28(4):344-53. PubMed ID: 16697155
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A robust boosting regression tree with applications in quantitative structure-activity relationship studies of organic compounds.
    Jiao J; Tan SM; Luo RM; Zhou YP
    J Chem Inf Model; 2011 Apr; 51(4):816-28. PubMed ID: 21417261
    [TBL] [Abstract][Full Text] [Related]  

  • 3. QSAR models for phosphoramidate prodrugs of 2'-methylcytidine as inhibitors of hepatitis C virus based on PSO boosting.
    Cheng Z; Zhang Y; Zhou C
    Chem Biol Drug Des; 2011 Dec; 78(6):948-59. PubMed ID: 21895985
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Prediction of toxicity of nitrobenzenes using ab initio and least squares support vector machines.
    Niazi A; Jameh-Bozorghi S; Nori-Shargh D
    J Hazard Mater; 2008 Mar; 151(2-3):603-9. PubMed ID: 17630186
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression.
    Yao XJ; Panaye A; Doucet JP; Zhang RS; Chen HF; Liu MC; Hu ZD; Fan BT
    J Chem Inf Comput Sci; 2004; 44(4):1257-66. PubMed ID: 15272833
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Simultaneously optimized support vector regression combined with genetic algorithm for QSAR analysis of KDR/VEGFR-2 inhibitors.
    Sun M; Chen J; Cai J; Cao M; Yin S; Ji M
    Chem Biol Drug Des; 2010 May; 75(5):494-505. PubMed ID: 20486936
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Feature selection and linear/nonlinear regression methods for the accurate prediction of glycogen synthase kinase-3beta inhibitory activities.
    Goodarzi M; Freitas MP; Jensen R
    J Chem Inf Model; 2009 Apr; 49(4):824-32. PubMed ID: 19338295
    [TBL] [Abstract][Full Text] [Related]  

  • 8. QSAR study of malonyl-CoA decarboxylase inhibitors using GA-MLR and a new strategy of consensus modeling.
    Li J; Lei B; Liu H; Li S; Yao X; Liu M; Gramatica P
    J Comput Chem; 2008 Dec; 29(16):2636-47. PubMed ID: 18484640
    [TBL] [Abstract][Full Text] [Related]  

  • 9. QSAR study of Akt/protein kinase B (PKB) inhibitors using support vector machine.
    Dong X; Jiang C; Hu H; Yan J; Chen J; Hu Y
    Eur J Med Chem; 2009 Oct; 44(10):4090-7. PubMed ID: 19497644
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Global, local and novel consensus quantitative structure-activity relationship studies of 4-(Phenylaminomethylene) isoquinoline-1, 3 (2H, 4H)-diones as potent inhibitors of the cyclin-dependent kinase 4.
    Lei B; Xi L; Li J; Liu H; Yao X
    Anal Chim Acta; 2009 Jun; 644(1-2):17-24. PubMed ID: 19463556
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Piecewise hypersphere modeling by particle swarm optimization in QSAR studies of bioactivities of chemical compounds.
    Lin WQ; Jiang JH; Shen Q; Wu HL; Shen GL; Yu RQ
    J Chem Inf Model; 2005; 45(3):535-41. PubMed ID: 15921443
    [TBL] [Abstract][Full Text] [Related]  

  • 12. QSAR studies on 4-anilino-3-quinolinecarbonitriles as Src kinase inhibitors using robust PCA and both linear and nonlinear models.
    Sun M; Zheng Y; Wei H; Chen J; Ji M
    J Enzyme Inhib Med Chem; 2009 Oct; 24(5):1109-16. PubMed ID: 19555174
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Molecule kernels: a descriptor- and alignment-free quantitative structure-activity relationship approach.
    Mohr JA; Jain BJ; Obermayer K
    J Chem Inf Model; 2008 Sep; 48(9):1868-81. PubMed ID: 18767832
    [TBL] [Abstract][Full Text] [Related]  

  • 14. QSAR study on 5-lipoxygenase inhibitors based on support vector machine.
    Niu B; Su Q; Yuan X; Lu W; Ding J
    Med Chem; 2012 Nov; 8(6):1108-16. PubMed ID: 22779798
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Determining the validity of a QSAR model--a classification approach.
    Guha R; Jurs PC
    J Chem Inf Model; 2005; 45(1):65-73. PubMed ID: 15667130
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Molecular connectivity indices for predicting bioactivities of substituted nitrobenzene and aniline compounds.
    Lin KH; Jaw CG; Yen JH; Wang YS
    Ecotoxicol Environ Saf; 2009 Oct; 72(7):1942-9. PubMed ID: 19423164
    [TBL] [Abstract][Full Text] [Related]  

  • 17. QSAR study of heparanase inhibitors activity using artificial neural networks and Levenberg-Marquardt algorithm.
    Jalali-Heravi M; Asadollahi-Baboli M; Shahbazikhah P
    Eur J Med Chem; 2008 Mar; 43(3):548-56. PubMed ID: 17602800
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Exploring QSAR for substituted 2-sulfonyl-phenyl-indol derivatives as potent and selective COX-2 inhibitors using different chemometrics tools.
    Khoshneviszadeh M; Edraki N; Miri R; Hemmateenejad B
    Chem Biol Drug Des; 2008 Dec; 72(6):564-74. PubMed ID: 19090923
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Exploration of QSAR modelling techniques and their combination to rationalize the physicochemical characters of nitrophenyl derivatives towards aldose reductase inhibition.
    Soni LK; Gupta AK; Kaskhedikar SG
    J Enzyme Inhib Med Chem; 2009 Aug; 24(4):1002-7. PubMed ID: 19514863
    [TBL] [Abstract][Full Text] [Related]  

  • 20. QSAR study of PETT derivatives as potent HIV-1 reverse transcriptase inhibitors.
    Sabet R; Fassihi A; Moeinifard B
    J Mol Graph Model; 2009 Sep; 28(2):146-55. PubMed ID: 19570701
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.