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Journal Abstract Search

109 related items for PubMed ID: 30762564

  • 1.
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  • 2. A Computational Model for Predicting RNase H Domain of Retrovirus.
    Wu S, Zhang X, Han J.
    PLoS One; 2016; 11(8):e0161913. PubMed ID: 27574780
    [Abstract] [Full Text] [Related]

  • 3. A computational model for predicting fusion peptide of retroviruses.
    Wu S, Han J, Liu R, Liu J, Lv H.
    Comput Biol Chem; 2016 Apr; 61():245-50. PubMed ID: 26963379
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  • 4. A computational approach identifies two regions of Hepatitis C Virus E1 protein as interacting domains involved in viral fusion process.
    Bruni R, Costantino A, Tritarelli E, Marcantonio C, Ciccozzi M, Rapicetta M, El Sawaf G, Giuliani A, Ciccaglione AR.
    BMC Struct Biol; 2009 Jul 29; 9():48. PubMed ID: 19640267
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  • 7. Conservation of a proteinase cleavage site between an insect retrovirus (gypsy) Env protein and a baculovirus envelope fusion protein.
    Pearson MN, Rohrmann GF.
    Virology; 2004 Apr 25; 322(1):61-8. PubMed ID: 15063117
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  • 8. A computational method for predicting nucleocapsid protein in retroviruses.
    Guo M, Ma Y, Liu W, Yuan Z.
    Sci Rep; 2022 Jan 11; 12(1):524. PubMed ID: 35017554
    [Abstract] [Full Text] [Related]

  • 9. Signal peptide discrimination and cleavage site identification using SVM and NN.
    Kazemian HB, Yusuf SA, White K.
    Comput Biol Med; 2014 Feb 11; 45():98-110. PubMed ID: 24480169
    [Abstract] [Full Text] [Related]

  • 10. Using the concept of Chou's pseudo amino acid composition to predict enzyme family classes: an approach with support vector machine based on discrete wavelet transform.
    Qiu JD, Huang JH, Shi SP, Liang RP.
    Protein Pept Lett; 2010 Jun 11; 17(6):715-22. PubMed ID: 19961429
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  • 11. Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties.
    Huang HL, Lin IC, Liou YF, Tsai CT, Hsu KT, Huang WL, Ho SJ, Ho SY.
    BMC Bioinformatics; 2011 Feb 15; 12 Suppl 1(Suppl 1):S47. PubMed ID: 21342579
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  • 13. Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences.
    Cai B, Jiang X.
    BMC Bioinformatics; 2016 Mar 03; 17():116. PubMed ID: 26940649
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  • 14. 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
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  • 15. A computational method for prediction of matrix proteins in endogenous retroviruses.
    Ma Y, Liu R, Lv H, Han J, Zhong D, Zhang X.
    PLoS One; 2017 May 18; 12(5):e0176909. PubMed ID: 28472185
    [Abstract] [Full Text] [Related]

  • 16. SVM-based prediction of caspase substrate cleavage sites.
    Wee LJ, Tan TW, Ranganathan S.
    BMC Bioinformatics; 2006 Dec 18; 7 Suppl 5(Suppl 5):S14. PubMed ID: 17254298
    [Abstract] [Full Text] [Related]

  • 17. Incorporating secondary features into the general form of Chou's PseAAC for predicting protein structural class.
    Liao B, Xiang Q, Li D.
    Protein Pept Lett; 2012 Nov 18; 19(11):1133-8. PubMed ID: 22185510
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  • 18. A fast method to predict protein interaction sites from sequences.
    Gallet X, Charloteaux B, Thomas A, Brasseur R.
    J Mol Biol; 2000 Sep 29; 302(4):917-26. PubMed ID: 10993732
    [Abstract] [Full Text] [Related]

  • 19. Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing.
    Su EC, Chang JM, Cheng CW, Sung TY, Hsu WL.
    BMC Bioinformatics; 2012 Sep 29; 13 Suppl 17(Suppl 17):S13. PubMed ID: 23282098
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