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Journal Abstract Search

134 related items for PubMed ID: 16253222

  • 1. Probabilistic prediction of protein-protein interactions from the protein sequences.
    Chinnasamy A, Mittal A, Sung WK.
    Comput Biol Med; 2006 Oct; 36(10):1143-54. PubMed ID: 16253222
    [Abstract] [Full Text] [Related]

  • 2. Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure.
    Lewis DP, Jebara T, Noble WS.
    Bioinformatics; 2006 Nov 15; 22(22):2753-60. PubMed ID: 16966363
    [Abstract] [Full Text] [Related]

  • 3. A mouse protein interactome through combined literature mining with multiple sources of interaction evidence.
    Li X, Cai H, Xu J, Ying S, Zhang Y.
    Amino Acids; 2010 Apr 15; 38(4):1237-52. PubMed ID: 19669079
    [Abstract] [Full Text] [Related]

  • 4. The context-tree kernel for strings.
    Cuturi M, Vert JP.
    Neural Netw; 2005 Oct 15; 18(8):1111-23. PubMed ID: 16198086
    [Abstract] [Full Text] [Related]

  • 5. Probabilistic prediction and ranking of human protein-protein interactions.
    Scott MS, Barton GJ.
    BMC Bioinformatics; 2007 Jul 05; 8():239. PubMed ID: 17615067
    [Abstract] [Full Text] [Related]

  • 6. A regularized discriminative model for the prediction of protein-peptide interactions.
    Lehrach WP, Husmeier D, Williams CK.
    Bioinformatics; 2006 Mar 01; 22(5):532-40. PubMed ID: 16397010
    [Abstract] [Full Text] [Related]

  • 7. Improving the performance of an SVM-based method for predicting protein-protein interactions.
    Dohkan S, Koike A, Takagi T.
    In Silico Biol; 2006 Mar 01; 6(6):515-29. PubMed ID: 17518762
    [Abstract] [Full Text] [Related]

  • 8. Integrated feature and parameter optimization for an evolving spiking neural network: exploring heterogeneous probabilistic models.
    Schliebs S, Defoin-Platel M, Worner S, Kasabov N.
    Neural Netw; 2009 Mar 01; 22(5-6):623-32. PubMed ID: 19615855
    [Abstract] [Full Text] [Related]

  • 9. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks.
    Werhli AV, Grzegorczyk M, Husmeier D.
    Bioinformatics; 2006 Oct 15; 22(20):2523-31. PubMed ID: 16844710
    [Abstract] [Full Text] [Related]

  • 10. An integrative approach for predicting interactions of protein regions.
    Schelhorn SE, Lengauer T, Albrecht M.
    Bioinformatics; 2008 Aug 15; 24(16):i35-41. PubMed ID: 18689837
    [Abstract] [Full Text] [Related]

  • 11. Prediction of turn types in protein structure by machine-learning classifiers.
    Meissner M, Koch O, Klebe G, Schneider G.
    Proteins; 2009 Feb 01; 74(2):344-52. PubMed ID: 18618702
    [Abstract] [Full Text] [Related]

  • 12. Improving protein protein interaction prediction based on phylogenetic information using a least-squares support vector machine.
    Craig RA, Liao L.
    Ann N Y Acad Sci; 2007 Dec 01; 1115():154-67. PubMed ID: 17925357
    [Abstract] [Full Text] [Related]

  • 13. Prediction of Ras-effector interactions using position energy matrices.
    Kiel C, Serrano L.
    Bioinformatics; 2007 Sep 01; 23(17):2226-30. PubMed ID: 17599936
    [Abstract] [Full Text] [Related]

  • 14. Network-based de-noising improves prediction from microarray data.
    Kato T, Murata Y, Miura K, Asai K, Horton PB, Koji T, Fujibuchi W.
    BMC Bioinformatics; 2006 Mar 20; 7 Suppl 1(Suppl 1):S4. PubMed ID: 16723007
    [Abstract] [Full Text] [Related]

  • 15. Bayesian methods for predicting interacting protein pairs using domain information.
    Kim I, Liu Y, Zhao H.
    Biometrics; 2007 Sep 20; 63(3):824-33. PubMed ID: 17825014
    [Abstract] [Full Text] [Related]

  • 16. Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure.
    Song J, Yuan Z, Tan H, Huber T, Burrage K.
    Bioinformatics; 2007 Dec 01; 23(23):3147-54. PubMed ID: 17942444
    [Abstract] [Full Text] [Related]

  • 17. An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies.
    Lancashire LJ, Lemetre C, Ball GR.
    Brief Bioinform; 2009 May 01; 10(3):315-29. PubMed ID: 19307287
    [Abstract] [Full Text] [Related]

  • 18. PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.
    Wang Y, You Z, Li X, Chen X, Jiang T, Zhang J.
    Int J Mol Sci; 2017 May 11; 18(5):. PubMed ID: 28492483
    [Abstract] [Full Text] [Related]

  • 19. PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data.
    Hawkins T, Chitale M, Luban S, Kihara D.
    Proteins; 2009 Feb 15; 74(3):566-82. PubMed ID: 18655063
    [Abstract] [Full Text] [Related]

  • 20. Bayesian networks for multivariate data analysis and prognostic modelling in cardiac surgery.
    Peek N, Verduijn M, Rosseel PM, de Jonge E, de Mol BA.
    Stud Health Technol Inform; 2007 Feb 15; 129(Pt 1):596-600. PubMed ID: 17911786
    [Abstract] [Full Text] [Related]

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