File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

TitlePrior knowledge based mining functional modules from Yeast PPI networks with gene ontology
Authors
Issue Date2010
Citation
BMC Bioinformatics, 2010, v. 11, suppl. 11, article no. S3 How to Cite?
AbstractBackground: In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships.Results: The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches.Conclusions: The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms. © 2010 Ng and Jing; licensee BioMed Central Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/276883
ISSN
2020 Impact Factor: 3.169
2020 SCImago Journal Rankings: 1.567
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJing, Liping-
dc.contributor.authorNg, Michael K.-
dc.date.accessioned2019-09-18T08:34:56Z-
dc.date.available2019-09-18T08:34:56Z-
dc.date.issued2010-
dc.identifier.citationBMC Bioinformatics, 2010, v. 11, suppl. 11, article no. S3-
dc.identifier.issn1471-2105-
dc.identifier.urihttp://hdl.handle.net/10722/276883-
dc.description.abstractBackground: In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships.Results: The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches.Conclusions: The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms. © 2010 Ng and Jing; licensee BioMed Central Ltd.-
dc.languageeng-
dc.relation.ispartofBMC Bioinformatics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titlePrior knowledge based mining functional modules from Yeast PPI networks with gene ontology-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/1471-2105-11-S11-S3-
dc.identifier.pmid21172053-
dc.identifier.pmcidPMC3024868-
dc.identifier.scopuseid_2-s2.0-78650802151-
dc.identifier.volume11-
dc.identifier.issuesuppl. 11-
dc.identifier.spagearticle no. S3-
dc.identifier.epagearticle no. S3-
dc.identifier.isiWOS:000290219200003-
dc.identifier.issnl1471-2105-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats