Variable context Markov chains for HIV protease cleavage site prediction
Author:
Publisher
Elsevier BV
Subject
Applied Mathematics,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine,Statistics and Probability
Reference21 articles.
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2. Variations on probabilistic suffix trees: statistical modeling and prediction of protein families;Bejerano;Bioinformatics,2001
3. Identification of transcription factor binding sites with variable-order Bayesian networks;Ben-Gal;Bioinformatics,2005
4. Variable length Markov chains;Buhlman;Annals of Statistics,1999
5. Artificial neural network model for predicting HIV protease cleavage sites in protein;Cai;Advances in Engineering Software,1998
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1. HIV-1 Protease Cleavages;2021 International Conference on Innovative Computing (ICIC);2021-11-09
2. Performance Analysis of Deep CNN Assisted Optimized HIV-I Protease Cleavage Site Prediction with Hybridized Technique;Lecture Notes in Electrical Engineering;2021
3. Incorporating the Coevolving Information of Substrates in Predicting HIV-1 Protease Cleavage Sites;IEEE/ACM Transactions on Computational Biology and Bioinformatics;2020-11-01
4. The identification of variable-length coevolutionary patterns for predicting HIV-1 protease cleavage sites;2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC);2020-10-11
5. Compositional framework for multitask learning in the identification of cleavage sites of HIV-1 protease;Journal of Biomedical Informatics;2020-02
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