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 *

84 related articles for article (PubMed ID: 15446815)

  • 1. Heuristic extraction of rules in pruned artificial neural networks models used for quantifying highly overlapping chromatographic peaks.
    Hervás C; Silva M; Serrano JM; Orejuela E
    J Chem Inf Comput Sci; 2004; 44(5):1576-84. PubMed ID: 15446815
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Improving the quantification of highly overlapping chromatographic peaks by using product unit neural networks modeled by an evolutionary algorithm.
    Hervás C; Martínez AC; Silva M; Serrano JM
    J Chem Inf Model; 2005; 45(4):894-903. PubMed ID: 16045283
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Artificial neural network model for earthquake prediction with radon monitoring.
    Külahci F; Inceöz M; Doğru M; Aksoy E; Baykara O
    Appl Radiat Isot; 2009 Jan; 67(1):212-9. PubMed ID: 18789709
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Use of pruned computational neural networks for processing the response of oscillating chemical reactions with a view to analyzing nonlinear multicomponent mixtures.
    Hervás C; Toledo R; Silva M
    J Chem Inf Comput Sci; 2001; 41(4):1083-92. PubMed ID: 11500128
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Neural network explanation using inversion.
    Saad EW; Wunsch DC
    Neural Netw; 2007 Jan; 20(1):78-93. PubMed ID: 17029713
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Predicting conductance due to upconing using neural networks.
    Coppola EA; McLane CF; Poulton MM; Szidarovszky F; Magelky RD
    Ground Water; 2005; 43(6):827-36. PubMed ID: 16324004
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Application of serum protein fingerprinting coupled with artificial neural network model in diagnosis of hepatocellular carcinoma.
    Wang JX; Zhang B; Yu JK; Liu J; Yang MQ; Zheng S
    Chin Med J (Engl); 2005 Aug; 118(15):1278-84. PubMed ID: 16117882
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Generating rules with predicates, terms and variables from the pruned neural networks.
    Nayak R
    Neural Netw; 2009 May; 22(4):405-14. PubMed ID: 19269778
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of the concentration of chlorophyll-a for Liuhai urban lakes in Beijing City.
    Zeng Y; Yang ZF; Liu JL
    J Environ Sci (China); 2006; 18(4):827-31. PubMed ID: 17078569
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Prediction of liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome using artificial neural networks.
    Shinoda K; Sugimoto M; Yachie N; Sugiyama N; Masuda T; Robert M; Soga T; Tomita M
    J Proteome Res; 2006 Dec; 5(12):3312-7. PubMed ID: 17137332
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Application of artificial neural networks to establish a predictive mortality risk model in children admitted to a paediatric intensive care unit.
    Chan CH; Chan EY; Ng DK; Chow PY; Kwok KL
    Singapore Med J; 2006 Nov; 47(11):928-34. PubMed ID: 17075658
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Spectrophotometric resolution of ternary mixtures of tryptophan, tyrosine, and histidine with the aid of principal component-artificial neural network models.
    Hasani M; Moloudi M; Emami F
    Anal Biochem; 2007 Nov; 370(1):68-76. PubMed ID: 17662683
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Artificial neural networks in analysis of indinavir and its degradation products retention.
    Jancić-Stojanović B; Ivanović D; Malenović A; Medenica M
    Talanta; 2009 Apr; 78(1):107-12. PubMed ID: 19174211
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Application of principal component-artificial neural network models for simultaneous determination of phenolic compounds by a kinetic spectrophotometric method.
    Hasani M; Moloudi M
    J Hazard Mater; 2008 Aug; 157(1):161-9. PubMed ID: 18272286
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Greedy rule generation from discrete data and its use in neural network rule extraction.
    Odajima K; Hayashi Y; Tianxia G; Setiono R
    Neural Netw; 2008 Sep; 21(7):1020-8. PubMed ID: 18442894
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Comparison of artificial neural network and multiple linear regression in the optimization of formulation parameters of leuprolide acetate loaded liposomes.
    Arulsudar N; Subramanian N; Muthy RS
    J Pharm Pharm Sci; 2005 Aug; 8(2):243-58. PubMed ID: 16124936
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A neural network model for predicting aquifer water level elevations.
    Coppola EA; Rana AJ; Poulton MM; Szidarovszky F; Uhl VW
    Ground Water; 2005; 43(2):231-41. PubMed ID: 15819944
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Retention prediction of adrenoreceptor agonists and antagonists on a diol column in hydrophilic interaction chromatography.
    Quiming NS; Denola NL; Ueta I; Saito Y; Tatematsu S; Jinno K
    Anal Chim Acta; 2007 Aug; 598(1):41-50. PubMed ID: 17693305
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 5.