Abstract
New methods for reliable quantitative analysis of biological network data are in high demand in today’s bioinformatics and systems biology. Here we demonstrate the applicability of the co-citation, developed earlier for the analysis of scientific literature for finding functionally similar nodes in protein-protein interaction networks in several model organisms. We prove the power of our approach in a novel way: the predicted closely related enzymes are compared to the closeness of their enzyme commission (EC) numbers, therefore we can numerically evaluate our prediction method. We have found clear correspondence between related enzymatic functions and high co-citation of proteins in interaction networks.
Keywords: Co-citation, protein interaction networks, vertex similarity.
Protein & Peptide Letters
Title:Characterizing the Functional Similarity of Enzymes with High Co-Citation in Interaction Networks
Volume: 20 Issue: 10
Author(s): Lilla Tothmeresz and Vince Grolmusz
Affiliation:
Keywords: Co-citation, protein interaction networks, vertex similarity.
Abstract: New methods for reliable quantitative analysis of biological network data are in high demand in today’s bioinformatics and systems biology. Here we demonstrate the applicability of the co-citation, developed earlier for the analysis of scientific literature for finding functionally similar nodes in protein-protein interaction networks in several model organisms. We prove the power of our approach in a novel way: the predicted closely related enzymes are compared to the closeness of their enzyme commission (EC) numbers, therefore we can numerically evaluate our prediction method. We have found clear correspondence between related enzymatic functions and high co-citation of proteins in interaction networks.
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Cite this article as:
Tothmeresz Lilla and Grolmusz Vince, Characterizing the Functional Similarity of Enzymes with High Co-Citation in Interaction Networks, Protein & Peptide Letters 2013; 20 (10) . https://dx.doi.org/10.2174/0929866511320100013
DOI https://dx.doi.org/10.2174/0929866511320100013 |
Print ISSN 0929-8665 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5305 |
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