File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1186/1471-2105-9-59
- Scopus: eid_2-s2.0-39749170516
- PMID: 18226195
- WOS: WOS:000253686900002
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification
Title | Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification |
---|---|
Authors | |
Issue Date | 2008 |
Publisher | BioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcbioinformatics/ |
Citation | Bmc Bioinformatics, 2008, v. 9 How to Cite? |
Abstract | Background: In the present investigation, we have used an exhaustive metabolite profiling approach to search for biomarkers in recombinant Aspergillus nidulans (mutants that produce the 6- methyl salicylic acid polyketide molecule) for application in metabolic engineering. Results: More than 450 metabolites were detected and subsequently used in the analysis. Our approach consists of two analytical steps of the metabolic profiling data, an initial non-linear unsupervised analysis with Self-Organizing Maps (SOM) to identify similarities and differences among the metabolic profiles of the studied strains, followed by a second, supervised analysis for training a classifier based on the selected biomarkers. Our analysis identified seven putative biomarkers that were able to cluster the samples according to their genotype. A Support Vector Machine was subsequently employed to construct a predictive model based on the seven biomarkers, capable of distinguishing correctly 14 out of the 16 samples of the different A. nidulans strains. Conclusion: Our study demonstrates that it is possible to use metabolite profiling for the classification of filamentous fungi as well as for the identification of metabolic engineering targets and draws the attention towards the development of a common database for storage of metabolomics data. © 2008 Kouskoumvekaki et al; licensee BioMed Central Ltd. |
Persistent Identifier | http://hdl.handle.net/10722/181248 |
ISSN | 2023 Impact Factor: 2.9 2023 SCImago Journal Rankings: 1.005 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kouskoumvekaki, I | en_US |
dc.contributor.author | Yang, Z | en_US |
dc.contributor.author | Jónsdóttir, SO | en_US |
dc.contributor.author | Olsson, L | en_US |
dc.contributor.author | Panagiotou, G | en_US |
dc.date.accessioned | 2013-02-21T02:03:28Z | - |
dc.date.available | 2013-02-21T02:03:28Z | - |
dc.date.issued | 2008 | en_US |
dc.identifier.citation | Bmc Bioinformatics, 2008, v. 9 | en_US |
dc.identifier.issn | 1471-2105 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/181248 | - |
dc.description.abstract | Background: In the present investigation, we have used an exhaustive metabolite profiling approach to search for biomarkers in recombinant Aspergillus nidulans (mutants that produce the 6- methyl salicylic acid polyketide molecule) for application in metabolic engineering. Results: More than 450 metabolites were detected and subsequently used in the analysis. Our approach consists of two analytical steps of the metabolic profiling data, an initial non-linear unsupervised analysis with Self-Organizing Maps (SOM) to identify similarities and differences among the metabolic profiles of the studied strains, followed by a second, supervised analysis for training a classifier based on the selected biomarkers. Our analysis identified seven putative biomarkers that were able to cluster the samples according to their genotype. A Support Vector Machine was subsequently employed to construct a predictive model based on the seven biomarkers, capable of distinguishing correctly 14 out of the 16 samples of the different A. nidulans strains. Conclusion: Our study demonstrates that it is possible to use metabolite profiling for the classification of filamentous fungi as well as for the identification of metabolic engineering targets and draws the attention towards the development of a common database for storage of metabolomics data. © 2008 Kouskoumvekaki et al; licensee BioMed Central Ltd. | en_US |
dc.language | eng | en_US |
dc.publisher | BioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcbioinformatics/ | en_US |
dc.relation.ispartof | BMC Bioinformatics | en_US |
dc.title | Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification | en_US |
dc.type | Article | en_US |
dc.identifier.email | Panagiotou, G: gipa@hku.hk | en_US |
dc.identifier.authority | Panagiotou, G=rp01725 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1186/1471-2105-9-59 | en_US |
dc.identifier.pmid | 18226195 | - |
dc.identifier.scopus | eid_2-s2.0-39749170516 | en_US |
dc.identifier.volume | 9 | en_US |
dc.identifier.isi | WOS:000253686900002 | - |
dc.publisher.place | United Kingdom | en_US |
dc.identifier.scopusauthorid | Kouskoumvekaki, I=6602787035 | en_US |
dc.identifier.scopusauthorid | Yang, Z=7405434139 | en_US |
dc.identifier.scopusauthorid | Jónsdóttir, SO=35566503500 | en_US |
dc.identifier.scopusauthorid | Olsson, L=7203077540 | en_US |
dc.identifier.scopusauthorid | Panagiotou, G=8566179700 | en_US |
dc.identifier.issnl | 1471-2105 | - |