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Reducing the complexity of complex gene coexpression networks by coupling multiweighted labeling with topological analysis.

Benso A, Cornale P, Di Carlo S, Politano G, Savino A - Biomed Res Int (2013)

Bottom Line: In order to infer relevant information, the network must be properly filtered and its complexity reduced.This paper proposes an efficient multivariate filtering designed to analyze the topological properties of a coexpression network in order to identify potential relevant genes for a given disease.Results have been validated resorting to bibliographic data automatically mined using the ProteinQuest literature mining tool.

View Article: PubMed Central - PubMed

Affiliation: Department of Controls and Computer Engineering, Politecnico di Torino, 10129 Torino, Italy ; Consorzio Interuniversitario Nazionale per l'Informatica, 11029 Verres, Italy.

ABSTRACT
Undirected gene coexpression networks obtained from experimental expression data coupled with efficient computational procedures are increasingly used to identify potentially relevant biological information (e.g., biomarkers) for a particular disease. However, coexpression networks built from experimental expression data are in general large highly connected networks with an elevated number of false-positive interactions (nodes and edges). In order to infer relevant information, the network must be properly filtered and its complexity reduced. Given the complexity and the multivariate nature of the information contained in the network, this requires the development and application of efficient feature selection algorithms to be able to exploit the topological characteristics of the network to identify relevant nodes and edges. This paper proposes an efficient multivariate filtering designed to analyze the topological properties of a coexpression network in order to identify potential relevant genes for a given disease. The algorithm has been tested on three datasets for three well known and studied diseases: acute myeloid leukemia, breast cancer, and diffuse large B-cell lymphoma. Results have been validated resorting to bibliographic data automatically mined using the ProteinQuest literature mining tool.

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Related in: MedlinePlus

AML preliminary bibliometric validation.
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fig2: AML preliminary bibliometric validation.

Mentions: Starting from these citation data, Figure 2 summarizes the preliminary validation performed for the AML dataset. Citation data have been filtered to select the AML filtered genes, only. Resulting data have been sorted by the AML citation rank and the citation rank of each selected gene for each of the three diseases has been plotted. The three citation ranks for each gene are always vertically aligned.


Reducing the complexity of complex gene coexpression networks by coupling multiweighted labeling with topological analysis.

Benso A, Cornale P, Di Carlo S, Politano G, Savino A - Biomed Res Int (2013)

AML preliminary bibliometric validation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814072&req=5

fig2: AML preliminary bibliometric validation.
Mentions: Starting from these citation data, Figure 2 summarizes the preliminary validation performed for the AML dataset. Citation data have been filtered to select the AML filtered genes, only. Resulting data have been sorted by the AML citation rank and the citation rank of each selected gene for each of the three diseases has been plotted. The three citation ranks for each gene are always vertically aligned.

Bottom Line: In order to infer relevant information, the network must be properly filtered and its complexity reduced.This paper proposes an efficient multivariate filtering designed to analyze the topological properties of a coexpression network in order to identify potential relevant genes for a given disease.Results have been validated resorting to bibliographic data automatically mined using the ProteinQuest literature mining tool.

View Article: PubMed Central - PubMed

Affiliation: Department of Controls and Computer Engineering, Politecnico di Torino, 10129 Torino, Italy ; Consorzio Interuniversitario Nazionale per l'Informatica, 11029 Verres, Italy.

ABSTRACT
Undirected gene coexpression networks obtained from experimental expression data coupled with efficient computational procedures are increasingly used to identify potentially relevant biological information (e.g., biomarkers) for a particular disease. However, coexpression networks built from experimental expression data are in general large highly connected networks with an elevated number of false-positive interactions (nodes and edges). In order to infer relevant information, the network must be properly filtered and its complexity reduced. Given the complexity and the multivariate nature of the information contained in the network, this requires the development and application of efficient feature selection algorithms to be able to exploit the topological characteristics of the network to identify relevant nodes and edges. This paper proposes an efficient multivariate filtering designed to analyze the topological properties of a coexpression network in order to identify potential relevant genes for a given disease. The algorithm has been tested on three datasets for three well known and studied diseases: acute myeloid leukemia, breast cancer, and diffuse large B-cell lymphoma. Results have been validated resorting to bibliographic data automatically mined using the ProteinQuest literature mining tool.

Show MeSH
Related in: MedlinePlus