Abstract
The impact of cancer in the society has created the necessity of new and faster theoretical models that may allow earlier cancer detection. The present review gives the prediction of cancer by using the star graphs of the protein sequences and proteome mass spectra by building a Quantitative Protein - Disease Relationships (QPDRs), similar to Quantitative Structure Activity Relationship (QSAR) models. The nodes of these star graphs are represented by the amino acids of each protein or by the amplitudes of the mass spectra signals and the edged are the geometric and/or functional relationships between the nodes. The star graphs can be numerically described by the invariant values named topological indices (TIs). The transformation of the star graphs (graphical representation) of proteins into TIs (numbers) facilitates the manipulation of protein information and the search for structure-function relationships in Proteomics. The advantages of this method include simplicity, fast calculations and free resources such as S2SNet and MARCH-INSIDE tools. Thus, this ideal theoretical scheme can be easily extended to other types of diseases or even other fields, such as Genomics or Systems Biology.
Keywords: Graphs, cancer prediction, linear discriminant analysis, complex network, quantitative protein - disease relationship
Current Proteomics
Title: Star Graphs of Protein Sequences and Proteome Mass Spectra in Cancer Prediction
Volume: 6 Issue: 4
Author(s): Jose M. Vazquez, Vanessa Aguiar, Jose A. Seoane, Ana Freire, Jose A. Serantes, Julian Dorado, Alejandro Pazos and Cristian R. Munteanu
Affiliation:
Keywords: Graphs, cancer prediction, linear discriminant analysis, complex network, quantitative protein - disease relationship
Abstract: The impact of cancer in the society has created the necessity of new and faster theoretical models that may allow earlier cancer detection. The present review gives the prediction of cancer by using the star graphs of the protein sequences and proteome mass spectra by building a Quantitative Protein - Disease Relationships (QPDRs), similar to Quantitative Structure Activity Relationship (QSAR) models. The nodes of these star graphs are represented by the amino acids of each protein or by the amplitudes of the mass spectra signals and the edged are the geometric and/or functional relationships between the nodes. The star graphs can be numerically described by the invariant values named topological indices (TIs). The transformation of the star graphs (graphical representation) of proteins into TIs (numbers) facilitates the manipulation of protein information and the search for structure-function relationships in Proteomics. The advantages of this method include simplicity, fast calculations and free resources such as S2SNet and MARCH-INSIDE tools. Thus, this ideal theoretical scheme can be easily extended to other types of diseases or even other fields, such as Genomics or Systems Biology.
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Cite this article as:
Vazquez M. Jose, Aguiar Vanessa, Seoane A. Jose, Freire Ana, Serantes A. Jose, Dorado Julian, Pazos Alejandro and Munteanu R. Cristian, Star Graphs of Protein Sequences and Proteome Mass Spectra in Cancer Prediction, Current Proteomics 2009; 6 (4) . https://dx.doi.org/10.2174/157016409789973752
DOI https://dx.doi.org/10.2174/157016409789973752 |
Print ISSN 1570-1646 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6247 |
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Mass spectrometry data acquisition and analysis for proteomics
The Thematic Issue on "Mass spectrometry data acquisition and analysis for proteomics" aims to explore the latest advancements and challenges in the field of proteomics through the lens of mass spectrometry. Proteomics, the large-scale study of proteins and their functions, plays a crucial role in understanding various biological processes and ...read more
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