BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

213 related articles for article (PubMed ID: 17459530)

  • 1. Classification of estrogen receptor-beta ligands on the basis of their binding affinities using support vector machine and linear discriminant analysis.
    Luan F; Liu HT; Ma WP; Fan BT
    Eur J Med Chem; 2008 Jan; 43(1):43-52. PubMed ID: 17459530
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis.
    Luan F; Zhang R; Zhao C; Yao X; Liu M; Hu Z; Fan B
    Chem Res Toxicol; 2005 Feb; 18(2):198-203. PubMed ID: 15720123
    [TBL] [Abstract][Full Text] [Related]  

  • 3. QSAR method for prediction of protein-peptide binding affinity: application to MHC class I molecule HLA-A*0201.
    Zhao C; Zhang H; Luan F; Zhang R; Liu M; Hu Z; Fan B
    J Mol Graph Model; 2007 Jul; 26(1):246-54. PubMed ID: 17275373
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Classification of inhibitors of protein tyrosine phosphatase 1B using molecular structure based descriptors.
    Patankar SJ; Jurs PC
    J Chem Inf Comput Sci; 2003; 43(3):885-99. PubMed ID: 12767147
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Classification study of skin sensitizers based on support vector machine and linear discriminant analysis.
    Ren Y; Liu H; Xue C; Yao X; Liu M; Fan B
    Anal Chim Acta; 2006 Jul; 572(2):272-82. PubMed ID: 17723489
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Quantitative prediction of logk of peptides in high-performance liquid chromatography based on molecular descriptors by using the heuristic method and support vector machine.
    Liu HX; Xue CX; Zhang RS; Yao XJ; Liu MC; Hu ZD; Fan BT
    J Chem Inf Comput Sci; 2004; 44(6):1979-86. PubMed ID: 15554667
    [TBL] [Abstract][Full Text] [Related]  

  • 7. CARSVM: a class association rule-based classification framework and its application to gene expression data.
    Kianmehr K; Alhajj R
    Artif Intell Med; 2008 Sep; 44(1):7-25. PubMed ID: 18586476
    [TBL] [Abstract][Full Text] [Related]  

  • 8. QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine.
    Xue CX; Zhang RS; Liu HX; Yao XJ; Liu MC; Hu ZD; Fan BT
    J Chem Inf Comput Sci; 2004; 44(5):1693-700. PubMed ID: 15446828
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A new descriptor selection scheme for SVM in unbalanced class problem: a case study using skin sensitisation dataset.
    Li S; Fedorowicz A; Andrew ME
    SAR QSAR Environ Res; 2007; 18(5-6):423-41. PubMed ID: 17654333
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Using classification structure pharmacokinetic relationship (SCPR) method to predict drug bioavailability based on grid-search support vector machine.
    Wang J; Du H; Yao X; Hu Z
    Anal Chim Acta; 2007 Oct; 601(2):156-63. PubMed ID: 17920387
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Classification structure-activity relationship (CSAR) studies for prediction of genotoxicity of thiophene derivatives.
    Du H; Wang J; Watzl J; Zhang X; Hu Z
    Toxicol Lett; 2008 Feb; 177(1):10-9. PubMed ID: 18243595
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity.
    Pourbasheer E; Riahi S; Ganjali MR; Norouzi P
    Eur J Med Chem; 2009 Dec; 44(12):5023-8. PubMed ID: 19837488
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Prediction of CCR5 receptor binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas based on the heuristic method, support vector machine and projection pursuit regression.
    Yuan Y; Zhang R; Hu R; Ruan X
    Eur J Med Chem; 2009 Jan; 44(1):25-34. PubMed ID: 18433938
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: concerns to support vector machine.
    Riahi S; Pourbasheer E; Ganjali MR; Norouzi P
    J Hazard Mater; 2009 Jul; 166(2-3):853-9. PubMed ID: 19144466
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Prediction of fungicidal activities of rice blast disease based on least-squares support vector machines and project pursuit regression.
    Du H; Wang J; Hu Z; Yao X; Zhang X
    J Agric Food Chem; 2008 Nov; 56(22):10785-92. PubMed ID: 18950187
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Quantitative structure-activity relationship models for prediction of sensory irritants (logRD50) of volatile organic chemicals.
    Luan F; Ma W; Zhang X; Zhang H; Liu M; Hu Z; Fan BT
    Chemosphere; 2006 May; 63(7):1142-53. PubMed ID: 16307788
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Support vector machine and the heuristic method to predict the solubility of hydrocarbons in electrolyte.
    Ma W; Zhang X; Luan F; Zhang H; Zhang R; Liu M; Hu Z; Fan BT
    J Phys Chem A; 2005 Apr; 109(15):3485-92. PubMed ID: 16833686
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods.
    Zang Q; Rotroff DM; Judson RS
    J Chem Inf Model; 2013 Dec; 53(12):3244-61. PubMed ID: 24279462
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Application of support vector machine (SVM) for prediction toxic activity of different data sets.
    Zhao CY; Zhang HX; Zhang XY; Liu MC; Hu ZD; Fan BT
    Toxicology; 2006 Jan; 217(2-3):105-19. PubMed ID: 16213080
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.