Multi-marker tagging single nucleotide polymorphism selection using estimation of distribution algorithms.
Objectives: This paper presents an optimization algorithm for the automatic selection of a minimal subset of tagging single nucleotide polymorphisms (SNPs).Methods and Materials: The determination of the set of minimal tagging SNPs is approached as an optimization problem in which each tagged SNP ca...
| Publicado en: | Artificial Intelligence in Medicine Vol. 50; no. 3; pp. 193 - 202 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Nov2010
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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=104943976&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104943976 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Nov2010 vid: 50 iid: 3 pid: 1004 pub: Elsevier B.V. artinfo: ui: 104943976 NLM20650616 2010858425 10.1016/j.artmed.2010.05.010 NLM20650616 104943976 ppf: 193 ppct: 9 formats: tig: atl: Multi-marker tagging single nucleotide polymorphism selection using estimation of distribution algorithms. aug: au: Santana R Mendiburu A Zaitlen N Eskin E Lozano JA Santana, Roberto Mendiburu, Alexander Zaitlen, Noah Eskin, Eleazar Lozano, Jose A affil: Faculty of Informatics, Universidad Politécnica de Madrid, R. 3306, Campus de Montegancedo, 28660 Boadilla del Monte, Madrid, Spain sug: subj: Algorithms Polymorphism, Genetic Statistics ab: Objectives: This paper presents an optimization algorithm for the automatic selection of a minimal subset of tagging single nucleotide polymorphisms (SNPs).Methods and Materials: The determination of the set of minimal tagging SNPs is approached as an optimization problem in which each tagged SNP can be covered by a single tagging SNP or by a pair of tagging SNPs. The problem is solved using an estimation of distribution algorithm (EDA) which takes advantage of the underlying topological structure defined by the SNP correlations to model the problem interactions. The EDA stochastically searches the constrained space of feasible solutions. It is evaluated across HapMap reference panel data sets.Results: The EDA was compared with a SAT solver, able to find the single-marker minimal tagging sets, and with the Tagger program. The percentage of reduction ranged from 10% to 43% in the number of tagging SNPs of the minimal multi-marker tagging set found by the EDA with respect to the other algorithms.Conclusions: The introduced algorithm is effective for the identification of minimal multi-marker SNP sets, which considerably reduce the dimension of the tagging SNP set in comparison with single-marker sets. Other variants of the SNP problem can be treated following the same approach. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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