File Download
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1186/1471-2105-10-S1-S19
- Scopus: eid_2-s2.0-60849090745
- PMID: 19208118
- WOS: WOS:000265601900019
Supplementary
- Citations:
- Appears in Collections:
Conference Paper: A voting approach to identify a small number of highly predictive genes using multiple classifiers
Title | A voting approach to identify a small number of highly predictive genes using multiple classifiers |
---|---|
Authors | |
Issue Date | 2009 |
Citation | Seventh Asia-Pacific Bioinformatics Conference (APBC 2009), Beijing, China, 13-16 January 2009. In BMC Bioinformatics, 2009, v. 10, n. SUPPL. 1 How to Cite? |
Abstract | Background: Microarray gene expression profiling has provided extensive datasets that can describe characteristics of cancer patients. An important challenge for this type of data is the discovery of gene sets which can be used as the basis of developing a clinical predictor for cancer. It is desirable that such gene sets be compact, give accurate predictions across many classifiers, be biologically relevant and have good biological process coverage. Results: By using a new type of multiple classifier voting approach, we have identified gene sets that can predict breast cancer prognosis accurately, for a range of classification algorithms. Unlike a wrapper approach, our method is not specialised towards a single classification technique. Experimental analysis demonstrates higher prediction accuracies for our sets of genes compared to previous work in the area. Moreover, our sets of genes are generally more compact than those previously proposed. Taking a biological viewpoint, from the literature, most of the genes in our sets are known to be strongly related to cancer. Conclusion: We show that it is possible to obtain superior classification accuracy with our approach and obtain a compact gene set that is also biologically relevant and has good coverage of different biological processes. © 2009 Hassan et al; licensee BioMed Central Ltd. |
Persistent Identifier | http://hdl.handle.net/10722/262622 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hassan, Md Rafiul | - |
dc.contributor.author | Hossain, M. Maruf | - |
dc.contributor.author | Bailey, James | - |
dc.contributor.author | Macintyre, Geoff | - |
dc.contributor.author | Ho, Joshua W.K. | - |
dc.contributor.author | Ramamohanarao, Kotagiri | - |
dc.date.accessioned | 2018-10-08T02:46:33Z | - |
dc.date.available | 2018-10-08T02:46:33Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | Seventh Asia-Pacific Bioinformatics Conference (APBC 2009), Beijing, China, 13-16 January 2009. In BMC Bioinformatics, 2009, v. 10, n. SUPPL. 1 | - |
dc.identifier.uri | http://hdl.handle.net/10722/262622 | - |
dc.description.abstract | Background: Microarray gene expression profiling has provided extensive datasets that can describe characteristics of cancer patients. An important challenge for this type of data is the discovery of gene sets which can be used as the basis of developing a clinical predictor for cancer. It is desirable that such gene sets be compact, give accurate predictions across many classifiers, be biologically relevant and have good biological process coverage. Results: By using a new type of multiple classifier voting approach, we have identified gene sets that can predict breast cancer prognosis accurately, for a range of classification algorithms. Unlike a wrapper approach, our method is not specialised towards a single classification technique. Experimental analysis demonstrates higher prediction accuracies for our sets of genes compared to previous work in the area. Moreover, our sets of genes are generally more compact than those previously proposed. Taking a biological viewpoint, from the literature, most of the genes in our sets are known to be strongly related to cancer. Conclusion: We show that it is possible to obtain superior classification accuracy with our approach and obtain a compact gene set that is also biologically relevant and has good coverage of different biological processes. © 2009 Hassan et al; licensee BioMed Central Ltd. | - |
dc.language | eng | - |
dc.relation.ispartof | BMC Bioinformatics | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | A voting approach to identify a small number of highly predictive genes using multiple classifiers | - |
dc.type | Conference_Paper | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1186/1471-2105-10-S1-S19 | - |
dc.identifier.pmid | 19208118 | - |
dc.identifier.scopus | eid_2-s2.0-60849090745 | - |
dc.identifier.volume | 10 | - |
dc.identifier.issue | SUPPL. 1 | - |
dc.identifier.spage | null | - |
dc.identifier.epage | null | - |
dc.identifier.eissn | 1471-2105 | - |
dc.identifier.isi | WOS:000265601900019 | - |
dc.identifier.issnl | 1471-2105 | - |