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Journal Abstract Search

378 related items for PubMed ID: 25375323

  • 1. Prediction of interactions between viral and host proteins using supervised machine learning methods.
    Barman RK, Saha S, Das S.
    PLoS One; 2014; 9(11):e112034. PubMed ID: 25375323
    [Abstract] [Full Text] [Related]

  • 2. Predicting protein-protein interactions between human and hepatitis C virus via an ensemble learning method.
    Emamjomeh A, Goliaei B, Zahiri J, Ebrahimpour R.
    Mol Biosyst; 2014 Dec; 10(12):3147-54. PubMed ID: 25230581
    [Abstract] [Full Text] [Related]

  • 3. An improved method for predicting interactions between virus and human proteins.
    Kim B, Alguwaizani S, Zhou X, Huang DS, Park B, Han K.
    J Bioinform Comput Biol; 2017 Feb; 15(1):1650024. PubMed ID: 27397631
    [Abstract] [Full Text] [Related]

  • 4. Identification of infectious disease-associated host genes using machine learning techniques.
    Barman RK, Mukhopadhyay A, Maulik U, Das S.
    BMC Bioinformatics; 2019 Dec 27; 20(1):736. PubMed ID: 31881961
    [Abstract] [Full Text] [Related]

  • 5. Heterogeneous data integration by tree-augmented naïve Bayes for protein-protein interactions prediction.
    Lin X, Chen XW.
    Proteomics; 2013 Jan 27; 13(2):261-8. PubMed ID: 23112070
    [Abstract] [Full Text] [Related]

  • 6. A generalized approach to predicting protein-protein interactions between virus and host.
    Zhou X, Park B, Choi D, Han K.
    BMC Genomics; 2018 Aug 13; 19(Suppl 6):568. PubMed ID: 30367586
    [Abstract] [Full Text] [Related]

  • 7. Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder.
    Fu Y, Guo Y, Wang Y, Luo J, Pu X, Li M, Zhang Z.
    Comput Biol Chem; 2015 Jun 13; 56():41-8. PubMed ID: 25854804
    [Abstract] [Full Text] [Related]

  • 8. Seminal quality prediction using data mining methods.
    Sahoo AJ, Kumar Y.
    Technol Health Care; 2014 Jun 13; 22(4):531-45. PubMed ID: 24898862
    [Abstract] [Full Text] [Related]

  • 9. Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.
    Park B, Im J, Tuvshinjargal N, Lee W, Han K.
    Comput Methods Programs Biomed; 2014 Nov 13; 117(2):158-67. PubMed ID: 25113160
    [Abstract] [Full Text] [Related]

  • 10. RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences.
    An JY, You ZH, Meng FR, Xu SJ, Wang Y.
    Int J Mol Sci; 2016 May 18; 17(5):. PubMed ID: 27213337
    [Abstract] [Full Text] [Related]

  • 11. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.
    Zhang M, Su Q, Lu Y, Zhao M, Niu B.
    Med Chem; 2017 May 18; 13(6):506-514. PubMed ID: 28530547
    [Abstract] [Full Text] [Related]

  • 12. Ensemble learning prediction of protein-protein interactions using proteins functional annotations.
    Saha I, Zubek J, Klingström T, Forsberg S, Wikander J, Kierczak M, Maulik U, Plewczynski D.
    Mol Biosyst; 2014 Apr 18; 10(4):820-30. PubMed ID: 24469380
    [Abstract] [Full Text] [Related]

  • 13. Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines.
    Majid A, Ali S, Iqbal M, Kausar N.
    Comput Methods Programs Biomed; 2014 Mar 18; 113(3):792-808. PubMed ID: 24472367
    [Abstract] [Full Text] [Related]

  • 14. Assessing the druggability of protein-protein interactions by a supervised machine-learning method.
    Sugaya N, Ikeda K.
    BMC Bioinformatics; 2009 Aug 25; 10():263. PubMed ID: 19703312
    [Abstract] [Full Text] [Related]

  • 15. A discriminative approach for identifying domain-domain interactions from protein-protein interactions.
    Zhao XM, Chen L, Aihara K.
    Proteins; 2010 Apr 25; 78(5):1243-53. PubMed ID: 20027642
    [Abstract] [Full Text] [Related]

  • 16. Signal peptide discrimination and cleavage site identification using SVM and NN.
    Kazemian HB, Yusuf SA, White K.
    Comput Biol Med; 2014 Feb 25; 45():98-110. PubMed ID: 24480169
    [Abstract] [Full Text] [Related]

  • 17. Detecting protein-protein interactions with a novel matrix-based protein sequence representation and support vector machines.
    You ZH, Li J, Gao X, He Z, Zhu L, Lei YK, Ji Z.
    Biomed Res Int; 2015 Feb 25; 2015():867516. PubMed ID: 26000305
    [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. Feature-based classification of native and non-native protein-protein interactions: Comparing supervised and semi-supervised learning approaches.
    Zhao N, Pang B, Shyu CR, Korkin D.
    Proteomics; 2011 Nov 11; 11(22):4321-30. PubMed ID: 22002942
    [Abstract] [Full Text] [Related]

  • 20. Prediction of different types of liver diseases using rule based classification model.
    Kumar Y, Sahoo G.
    Technol Health Care; 2013 Nov 11; 21(5):417-32. PubMed ID: 23963359
    [Abstract] [Full Text] [Related]

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