Brusic, V., G. Rudy, M. Honeyman, J. Hammer, and L. Harrison. 1998. Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 14:121.  Home/Search   Document Not in Database   Summary   Related Articles   Check

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Vladimir Brusic, George Rudy, Marco Honeyman, Jurgen Hammer, and Leonard Harrison. Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics, 14(2):121--130, 1998. ga98aVBrusic.

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Vladimir Brusic, George Rudy, Marco Honeyman, Jurgen Hammer, and Leonard Harrison. Prediction of MHC class II-binding peptides using an evolutionary algorithm and articial neural network. Bioinformatics, 14(2):121-130, 1998. ga98aVBrusic.

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Vladimir Brusic, George Rudy, Marco Honeyman, Jurgen Hammer, and Leonard Harrison. Prediction of MHC class II-binding peptides using an evolutionary algorithm and articial neural network. Bioinformatics, 14(2):121-130, 1998. ga98aVBrusic.

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Vladimir Brusic, George Rudy, Marco Honeyman, Jurgen Hammer, and Leonard Harrison. Prediction of MHC class II-binding peptides using an evolutionary algorithm and articial neural network. Bioinformatics, 14(2):121-130, 1998. ga98aVBrusic. Bibliography 89

....of specific targets of immune responses is important, but is becoming increasingly difficult as we learn more about the complexity of the domain. Computational models of immune interactions include those that utilize binding motifs [6] quantitative matrices [7] artificial neural networks (ANNs) [8], hidden Markov models (HMMs) 9] and molecular modeling [10] Computational models have been used in immunology to a) minimize the number of experiments required for the determination of targets of immune responses, or b) to conduct large scale computational simulations facilitating knowledge ....

....to MHC molecules, b) correlation of results between different methods is imperfect, c) sensitivity between different methods varies, and d) presence of noise and errors. Peptide binding to MHC molecules is therefore best described by using fuzzy measures of high , moderate , low or no affinity [8]. We used a fully connected three layer feed forward ANN using PlaNet software package [12] as described previously [8,13] The information about peptides that bind specific MHC molecule variants was extracted from MHCPEP database [4] literature, or collaborative data sharing. Peptide sequences ....

[Article contains additional citation context not shown here]

Brusic V., Rudy G., Honeyman G., Hammer J. and Harrison L. (1998). Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics, 14, 121-130.

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Brusic, V., G. Rudy, M. Honeyman, J. Hammer, and L. Harrison. 1998. Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 14:121.

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