Basic Exploratory Proteins Analysis with Statistical Methods Applied on Structural Features

Eugenio Del Prete (), Serena Dotolo (), Anna Marabotti () and Angelo Facchiano ()
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Eugenio Del Prete: Institute of Food Science, National Research Council
Serena Dotolo: Institute of Food Science, National Research Council
Anna Marabotti: Institute of Food Science, National Research Council
Angelo Facchiano: Institute of Food Science, National Research Council

A chapter in Mathematical Models in Biology, 2015, pp 173-187 from Springer

Abstract: Abstract Exploratory Data Analysis (EDA) is an approach for summarizing and visualizing the important characteristics of a data set, in order to make a prearranged data screening and display multivariate data in a graphical way, to render them more comprehensible. Moreover, it reveals hidden aspects within the simple evaluations. In particular, EDA is suitable for datasets with comparable variables, as structural-geometrical protein features. In this work, we analyzed some proteins belonging to ten different architectural families. After retrieval, feature selection and normalization stages, the dataset has been processed by means of simple correlation, partial correlation and principal component analysis (PCA), highlighting family-independent or family-specific relationships, and possible outliers for the dataset itself. The results can be useful to connect these features to functional protein properties.

Keywords: Correlation; Exploratory data analysis; Global features; Principal component analysis; Protein structure (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-319-23497-7_13

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