Prediction of MHC binding peptide using Gibbs motif sampler, weight matrix and artificial neural network
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چکیده
منابع مشابه
Prediction of MHC binding peptide using Gibbs motif sampler, weight matrix and artificial neural network
The identification of MHC restricted epitopes is an important goal in peptide based vaccine and diagnostic development. As wet lab experiments for identification of MHC binding peptide are expensive and time consuming, in silico tools have been developed as fast alternatives, however with low performance. In the present study, we used IEDB training and blind validation datasets for the predicti...
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Position Weight Matrix, Gibbs Sampler, and the Associated Significance Tests in Motif Characterization and Prediction
Position weight matrix (PWM) is not only one of the most widely used bioinformatic methods, but also a key component in more advanced computational algorithms (e.g., Gibbs sampler) for characterizing and discovering motifs in nucleotide or amino acid sequences. However, few generally applicable statistical tests are available for evaluating the significance of site patterns, PWM, and PWM scores...
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Traditional poultry production has changed to a considerable industry after few decades. Now, poultry industry is one of the main sectors to obtain the required protein for human consumption. Prediction of the weight and number of eggs according to economic traits can improve the efficiency of production and the profit of producers. In present study, the weight and number of eggs in Mazandaran ...
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Traditional poultry production has changed to a considerable industry after few decades. Now, poultry industry is one of the main sectors to obtain the required protein for human consumption. Prediction of the weight and number of eggs according to economic traits can improve the efficiency of production and the profit of producers. In present study, the weight and number of eggs in Mazandaran ...
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ژورنال
عنوان ژورنال: Bioinformation
سال: 2008
ISSN: 0973-8894,0973-2063
DOI: 10.6026/97320630003150