BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

108 related articles for article (PubMed ID: 17416337)

  • 1. Integrating subcellular location for improving machine learning models of remote homology detection in eukaryotic organisms.
    Shah AR; Oehmen CS; Harper J; Webb-Robertson BJ
    Comput Biol Chem; 2007 Apr; 31(2):138-42. PubMed ID: 17416337
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Remote protein homology detection and fold recognition using two-layer support vector machine classifiers.
    Muda HM; Saad P; Othman RM
    Comput Biol Med; 2011 Aug; 41(8):687-99. PubMed ID: 21704312
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Protein remote homology detection based on auto-cross covariance transformation.
    Liu X; Zhao L; Dong Q
    Comput Biol Med; 2011 Aug; 41(8):640-7. PubMed ID: 21664609
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A novel representation for apoptosis protein subcellular localization prediction using support vector machine.
    Zhang L; Liao B; Li D; Zhu W
    J Theor Biol; 2009 Jul; 259(2):361-5. PubMed ID: 19328812
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Robust prediction of protein subcellular localization combining PCA and WSVMs.
    Tian J; Gu H; Liu W; Gao C
    Comput Biol Med; 2011 Aug; 41(8):648-52. PubMed ID: 21722885
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Global sequence properties for superfamily prediction: a machine learning approach.
    Dobson RJ; Munroe PB; Caulfield MJ; Saqi MA
    J Integr Bioinform; 2009 Aug; 6(1):109. PubMed ID: 20134076
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of protein folds: extraction of new features, dimensionality reduction, and fusion of heterogeneous classifiers.
    Ghanty P; Pal NR
    IEEE Trans Nanobioscience; 2009 Mar; 8(1):100-10. PubMed ID: 19278932
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Remote homology detection using a kernel method that combines sequence and secondary-structure similarity scores.
    Wieser D; Niranjan M
    In Silico Biol; 2009; 9(3):89-103. PubMed ID: 19795568
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Beyond the Twilight Zone: automated prediction of structural properties of proteins by recursive neural networks and remote homology information.
    Mooney C; Pollastri G
    Proteins; 2009 Oct; 77(1):181-90. PubMed ID: 19422056
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Multilabel learning for protein subcellular location prediction.
    Li GZ; Wang X; Hu X; Liu JM; Zhao RW
    IEEE Trans Nanobioscience; 2012 Sep; 11(3):237-43. PubMed ID: 22987129
    [TBL] [Abstract][Full Text] [Related]  

  • 11. SubCellProt: predicting protein subcellular localization using machine learning approaches.
    Garg P; Sharma V; Chaudhari P; Roy N
    In Silico Biol; 2009; 9(1-2):35-44. PubMed ID: 19537160
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Two criteria for model selection in multiclass support vector machines.
    Wang L; Xue P; Chan KL
    IEEE Trans Syst Man Cybern B Cybern; 2008 Dec; 38(6):1432-48. PubMed ID: 19022717
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint descriptors.
    Geppert H; Humrich J; Stumpfe D; Gärtner T; Bajorath J
    J Chem Inf Model; 2009 Apr; 49(4):767-79. PubMed ID: 19309114
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Protein subcellular multi-localization prediction using a min-max modular support vector machine.
    Yang Y; Lu BL
    Int J Neural Syst; 2010 Feb; 20(1):13-28. PubMed ID: 20180250
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The irredundant class method for remote homology detection of protein sequences.
    Comin M; Verzotto D
    J Comput Biol; 2011 Dec; 18(12):1819-29. PubMed ID: 21548811
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Two multi-classification strategies used on SVM to predict protein structural classes by using auto covariance.
    Wu J; Li YZ; Li ML; Yu LZ
    Interdiscip Sci; 2009 Dec; 1(4):315-9. PubMed ID: 20640811
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A novel algorithm combining support vector machine with the discrete wavelet transform for the prediction of protein subcellular localization.
    Liang RP; Huang SY; Shi SP; Sun XY; Suo SB; Qiu JD
    Comput Biol Med; 2012 Feb; 42(2):180-7. PubMed ID: 22153357
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Multitask learning for protein subcellular location prediction.
    Xu Q; Pan SJ; Xue HH; Yang Q
    IEEE/ACM Trans Comput Biol Bioinform; 2011; 8(3):748-59. PubMed ID: 20421687
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Enhancing membrane protein subcellular localization prediction by parallel fusion of multi-view features.
    Yu D; Wu X; Shen H; Yang J; Tang Z; Qi Y; Yang J
    IEEE Trans Nanobioscience; 2012 Dec; 11(4):375-85. PubMed ID: 22875262
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Permutation importance: a corrected feature importance measure.
    Altmann A; Toloşi L; Sander O; Lengauer T
    Bioinformatics; 2010 May; 26(10):1340-7. PubMed ID: 20385727
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.