Essec\Faculty\Model\Contribution {#2233 ▼
#_index: "academ_contributions"
#_id: "10471"
#_source: array:26 [
"id" => "10471"
"slug" => "10471-qtl-mapping-using-a-memetic-algorithm-with-modifications-of-bic-as-fitness-function"
"yearMonth" => "2012-05"
"year" => "2012"
"title" => "QTL Mapping Using a Memetic Algorithm with Modifications of BIC as Fitness Function"
"description" => "FROMMLET, F., LJUBIC, I., BJÖRK ARNARDÓTTIR, H. et BOGDAN, M. (2012). QTL Mapping Using a Memetic Algorithm with Modifications of BIC as Fitness Function. <i>Statistical Applications in Genetics and Molecular Biology</i>, 11(4). 
FROMMLET, F., LJUBIC, I., BJÖRK ARNARDÓTTIR, H. et BOGDAN, M. (2012). QTL Mapping Using a Memetic Al "
"authors" => array:4 [
0 => array:3 [
"name" => "LJUBIC Ivana"
"bid" => "B00683004"
"slug" => "ljubic-ivana"
]
1 => array:1 [
"name" => "FROMMLET Florian"
]
2 => array:1 [
"name" => "BJÖRK ARNARDÓTTIR Helga"
]
3 => array:1 [
"name" => "BOGDAN Malgorzata"
]
]
"ouvrage" => ""
"keywords" => []
"updatedAt" => "2021-07-13 14:31:36"
"publicationUrl" => "https://www.researchgate.net/publication/225044299_QTL_Mapping_Using_a_Memetic_Algorithm_with_Modifications_of_BIC_as_Fitness_Function https://www.researchgate.net/publication/225044299_QTL_Mapping_Using_a_Memetic_Algorithm_with_Modifi "
"publicationInfo" => array:3 [
"pages" => null
"volume" => "11"
"number" => "4"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
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"abstract" => array:2 [
"fr" => "The problem of locating quantitative trait loci (QTL) for experimental populations can be approached by multiple regression analysis. In this context variable selection using a modification of the Bayesian Information Criterion (mBIC) has been well established in the past. In this article a memetic algorithm (MA) is introduced to find the model which minimizes the selection criterion. Apart from mBIC also a second modification (mBIC2) is considered, which has the property of controlling the false discovery rate. Given the Bayesian nature of our selection criteria, we are not only interested in finding the best model, but also in computing marker posterior probabilities using all models visited by MA. In a simulation study MA (with mBIC and mBIC2) is compared with a parallel genetic algorithm (PGA) which has been previously suggested for QTL mapping. It turns out that MA in combination with mBIC2 performs best, where determining QTL positions based on marker posterior probabilities yields even better results than using the best model selected by MA. Finally we consider a real data set from the literature and show that MA can also be extended to multiple interval mapping, which potentially increases the precision with which the exact location of QTLs can be estimated. The problem of locating quantitative trait loci (QTL) for experimental populations can be approached "
"en" => "The problem of locating quantitative trait loci (QTL) for experimental populations can be approached by multiple regression analysis. In this context variable selection using a modification of the Bayesian Information Criterion (mBIC) has been well established in the past. In this article a memetic algorithm (MA) is introduced to find the model which minimizes the selection criterion. Apart from mBIC also a second modification (mBIC2) is considered, which has the property of controlling the false discovery rate. Given the Bayesian nature of our selection criteria, we are not only interested in finding the best model, but also in computing marker posterior probabilities using all models visited by MA. In a simulation study MA (with mBIC and mBIC2) is compared with a parallel genetic algorithm (PGA) which has been previously suggested for QTL mapping. It turns out that MA in combination with mBIC2 performs best, where determining QTL positions based on marker posterior probabilities yields even better results than using the best model selected by MA. Finally we consider a real data set from the literature and show that MA can also be extended to multiple interval mapping, which potentially increases the precision with which the exact location of QTLs can be estimated. The problem of locating quantitative trait loci (QTL) for experimental populations can be approached "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-04-08T19:21:39.000Z"
"docTitle" => "QTL Mapping Using a Memetic Algorithm with Modifications of BIC as Fitness Function"
"docSurtitle" => "Articles"
"authorNames" => "<a href="/cv/ljubic-ivana">LJUBIC Ivana</a>, FROMMLET Florian, BJÖRK ARNARDÓTTIR Helga, BOGDAN Malgorzata <a href="/cv/ljubic-ivana">LJUBIC Ivana</a>, FROMMLET Florian, BJÖRK ARNARDÓTTIR Helga, BOGDAN Malgo "
"docDescription" => "<span class="document-property-authors">LJUBIC Ivana, FROMMLET Florian, BJÖRK ARNARDÓTTIR Helga, BOGDAN Malgorzata</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2012</span> <span class="document-property-authors">LJUBIC Ivana, FROMMLET Florian, BJÖRK ARNARDÓTTIR Helga, BOG "
"keywordList" => ""
"docPreview" => "<b>QTL Mapping Using a Memetic Algorithm with Modifications of BIC as Fitness Function</b><br><span>2012-05 | Articles </span> <b>QTL Mapping Using a Memetic Algorithm with Modifications of BIC as Fitness Function</b><br><span> "
"docType" => "research"
"publicationLink" => "<a href="https://www.researchgate.net/publication/225044299_QTL_Mapping_Using_a_Memetic_Algorithm_with_Modifications_of_BIC_as_Fitness_Function" target="_blank">QTL Mapping Using a Memetic Algorithm with Modifications of BIC as Fitness Function</a> <a href="https://www.researchgate.net/publication/225044299_QTL_Mapping_Using_a_Memetic_Algorithm_wi "
]
+lang: "fr"
+"_type": "_doc"
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}
