Abstract
Peptides are molecules of varying complexity, with different functions in the organism and with remarkable therapeutic interest. Predicting peptide activity by computational means can help us to understand their mechanism of action and deliver powerful drug-screening methodologies. In this chapter, we describe how to apply artificial neural networks to predict antimicrobial peptide activity.
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Acknowledgements
This work was supported by the European Union [grant number HEALTH-F3-2008-223414 to D.A. and FP7-PEOPLE-2012-IEF-330352 to M.T.], the Ministerio de Economía y Competitividad [grant number SAF2011-24899 to D.A.], and the Generalitat de Catalunya [grant number SGR2009-494 to D.A.]. M.T. would also like to acknowledge support from the Ramón y Cajal Program (Spain).
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Andreu, D., Torrent, M. (2015). Prediction of Bioactive Peptides Using Artificial Neural Networks. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 1260. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2239-0_7
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DOI: https://doi.org/10.1007/978-1-4939-2239-0_7
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