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http://hdl.handle.net/1942/34308
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DC Field | Value | Language |
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dc.contributor.author | Popovic, D. | - |
dc.contributor.author | Moschopoulos, C. | - |
dc.contributor.author | Sakai, R. | - |
dc.contributor.author | Sifrim, A. | - |
dc.contributor.author | AERTS, Jan | - |
dc.contributor.author | Moreau, Y. | - |
dc.contributor.author | DE MOOR, Bart | - |
dc.date.accessioned | 2021-06-21T09:56:50Z | - |
dc.date.available | 2021-06-21T09:56:50Z | - |
dc.date.issued | 2014 | - |
dc.date.submitted | 2021-03-22T13:50:21Z | - |
dc.identifier.citation | Proceedings - IEEE Symposium on Computer-Based Medical Systems, IEEE, p. 233 -238 | - |
dc.identifier.isbn | 9781479944354 | - |
dc.identifier.issn | 2372-9198 | - |
dc.identifier.uri | http://hdl.handle.net/1942/34308 | - |
dc.description.abstract | Recent developments in the field of -omics technologies brought great potential for conducting biomedical research in very efficient manner, but also raised a plethora of new computational challenges to be addressed. Extremely high dimensionality accompanied with poor signal-to-noise ratio and small sample size of data resulting from high-throughput experiments pose previously unprecedented problem, creating an increasing demand for innovative analytical strategies. In this work we propose an island model-based genetic algorithm for multivariate feature selection in the context of -omics data, which accommodates to a particular classification scenario via dynamic tuning of its parameters. We demonstrate it on two publicly available data sets containing gene expression profiles corresponding to the two distinct biomedical questions. We show that the algorithm consistently outperforms two additional feature selection schemes across data sets, regardless to which method is used in the subsequent classification step. | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.subject.other | genetic algorithm | - |
dc.subject.other | self-tuning | - |
dc.subject.other | island model | - |
dc.subject.other | feature selection | - |
dc.subject.other | biomarker discovery | - |
dc.title | A self-tuning genetic algorithm with applications in biomarker discovery | - |
dc.type | Proceedings Paper | - |
local.bibliographicCitation.conferencedate | MAY 27-29, 2014 | - |
local.bibliographicCitation.conferencename | 27th IEEE International Symposium on Computer-Based Medical Systems (CBMS) | - |
local.bibliographicCitation.conferenceplace | Icahn Sch Med, New York, NY | - |
dc.identifier.epage | 238 | - |
dc.identifier.spage | 233 | - |
local.bibliographicCitation.jcat | C1 | - |
local.publisher.place | 345 E 47TH ST, NEW YORK, NY 10017 USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Proceedings Paper | - |
dc.identifier.doi | 10.1109/CBMS.2014.10 | - |
dc.identifier.scopus | 2-s2.0-84907420793 | - |
dc.identifier.isi | WOS:000345222200046 | - |
dc.contributor.orcid | #NODATA# | - |
dc.contributor.orcid | #NODATA# | - |
dc.contributor.orcid | #NODATA# | - |
dc.contributor.orcid | #NODATA# | - |
dc.contributor.orcid | #NODATA# | - |
dc.contributor.orcid | #NODATA# | - |
dc.contributor.orcid | #NODATA# | - |
local.provider.type | Orcid | - |
local.bibliographicCitation.btitle | Proceedings - IEEE Symposium on Computer-Based Medical Systems | - |
local.uhasselt.uhpub | no | - |
item.contributor | Popovic, D. | - |
item.contributor | Moschopoulos, C. | - |
item.contributor | Sakai, R. | - |
item.contributor | Sifrim, A. | - |
item.contributor | AERTS, Jan | - |
item.contributor | Moreau, Y. | - |
item.contributor | DE MOOR, Bart | - |
item.fullcitation | Popovic, D.; Moschopoulos, C.; Sakai, R.; Sifrim, A.; AERTS, Jan; Moreau, Y. & DE MOOR, Bart (2014) A self-tuning genetic algorithm with applications in biomarker discovery. In: Proceedings - IEEE Symposium on Computer-Based Medical Systems, IEEE, p. 233 -238. | - |
item.accessRights | Closed Access | - |
item.fulltext | No Fulltext | - |
Appears in Collections: | Research publications |
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