Reducing the complexity of complex gene coexpression networks by coupling multiweighted labeling with topological analysis.
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 experi...
| Publicado en: | BioMed Research International Vol. 2013; pp. 676328 - 676329 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104113705&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104113705 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104113705 2012371314 NLM24222912 PMC3814072 104113705 ppf: 676328 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Reducing the complexity of complex gene coexpression networks by coupling multiweighted labeling with topological analysis. aug: au: Benso, Alfredo Cornale, Paolo Di Carlo, Stefano Politano, Gianfranco Savino, Alessandro affil: Department of Controls and Computer Engineering, Politecnico di Torino, 10129 Torino, Italy ; Consorzio Interuniversitario Nazionale per l'Informatica, 11029 Verres, Italy. sug: subj: Breast Neoplasms Bioinformatics Methods Leukemia, Myeloid, Acute Lymphoma, B-Cell Algorithms Breast Neoplasms Metabolism Female Gene Expression Profiling Genes Molecular Structure Leukemia, Myeloid, Acute Metabolism Lymphoma, B-Cell Metabolism Female ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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