Title:
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Applying discriminant and cluster analysis to separate allergenic from non-allergenic proteins
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Author:
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Naneva, L.; Nedyalkova, M.; Madurga Díez, Sergio; Mas i Pujadas, Francesc; Simeonov, V.
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Other authors:
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Universitat de Barcelona |
Abstract:
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As a result of increased healthcare requirements and the introduction of genetically modified foods, the problem of allergies is becoming a growing health problem. The concept of allergies has prompted the use of new methods such as genomics and proteomics to uncover the nature of allergies. In the present study, a selection of 1400 food proteins was analysed by PLS-DA (Partial Least Square-based Discriminant Analysis) after suitable transformation of structural parameters into uniform vectors. Then, the resulting strings of different length were converted into vectors with equal length by Auto and Cross-Covariance (ACC) analysis. Hierarchical and non-hierarchical (K-means) Cluster Analysis (CA) was also performed in order to reach a certain level of separation within a small training set of plant proteins (16 allergenic and 16 non-allergenic) using a new three-dimensional descriptor based on surface protein properties in combination with amino acid hydrophobicity scales. The novelty of the approach in protein differentiation into allergenic and non-allergenic classes is described in the article. The general goal of the present study was to show the effectiveness of a traditional chemometric method for classification (PLS-DA) and the options of Cluster Analysis (CA) to separate by multivariate statistical methods allergenic from non-allergenic proteins. |
Subject(s):
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-Aminoàcids -Anàlisi de conglomerats -Amino acids -Cluster analysis |
Rights:
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cc-by (c) Naneva, L. et al., 2019
http://creativecommons.org/licenses/by/3.0/es |
Document type:
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Article Article - Published version |
Published by:
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De Gruyter Open
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