Artículo
ALLERDET: A novel web app for prediction of protein allergenicity
Autor/es | García Moreno, Francisco M.
Gutiérrez Naranjo, Miguel Ángel ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2022 |
Fecha de depósito | 2024-04-22 |
Publicado en |
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Resumen | Allergic diseases are increasing around the world with unprecedented complexity and severity. One of the reasons is that genetically modified crops produce new potentially allergenic proteins. From this starting point, ... Allergic diseases are increasing around the world with unprecedented complexity and severity. One of the reasons is that genetically modified crops produce new potentially allergenic proteins. From this starting point, many researchers have paid attention to the development of tools to predict the allergenicity of new proteins. In this study, a novel approach is introduced for the prediction of food allergens based on Artificial Intelligence techniques: a pairwise sequence alignment with the FASTA program for feature extraction and the use of the Deep Learning technique known as Restricted Boltzmann Machines in combination with the Decision Tree method for the prediction process. The developed tool, called ALLERDET (publicly available at http://allerdet.frangam.com), overcomes the state-of-the-art methods. The performance of our method is: 98.46% sensitivity, 94.37% specificity and 97.26% accuracy), on a data set built from several publicly available sources. |
Cita | García Moreno, F.M. y Gutiérrez Naranjo, M.Á. (2022). ALLERDET: A novel web app for prediction of protein allergenicity. JOURNAL OF BIOMEDICAL INFORMATICS, 135, 104217. https://doi.org/10.1016/j.jbi.2022.104217. |
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2_ALLERDET_1-s2.0-S15320464220 ... | 1.461Mb | ![]() | Ver/ | |