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FARMS: a new algorithm for variable selection
dc.contributor.author | Pérez Álvarez, Susana |
dc.contributor.author | Gómez Melis, Guadalupe |
dc.contributor.author | Brander, Christian |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa |
dc.date.accessioned | 2016-04-21T14:44:57Z |
dc.date.available | 2016-04-21T14:44:57Z |
dc.date.issued | 2015-01-01 |
dc.identifier.citation | Pérez, S., Gomez, G., Brander, C. FARMS: a new algorithm for variable selection. "Biomed Research International", 01 Gener 2015, vol. 2015, núm. ID 319797, p. 1-11. |
dc.identifier.issn | 2314-6133 |
dc.identifier.uri | http://hdl.handle.net/2117/86075 |
dc.description.abstract | Large datasets including an extensive number of covariates are generated these days in many different situations, for instance, in detailed genetic studies of outbreed human populations or in complex analyses of immune responses to different infections. Aiming at informing clinical interventions or vaccine design, methods for variable selection identifying those variables with the optimal prediction performance for a specific outcome are crucial. However, testing for all potential subsets of variables is not feasible and alternatives to existing methods are needed. Here, we describe a new method to handle such complex datasets, referred to as FARMS, that combines forward and all subsets regression for model selection. We apply FARMS to a host genetic and immunological dataset of over 800 individuals from Lima (Peru) and Durban (South Africa) who were HIV infected and tested for antiviral immune responses. This dataset includes more than 500 explanatory variables: around 400 variables with information on HIV immune reactivity and around 100 individual genetic characteristics. We have implemented FARMS in R statistical language and we showed that FARMS is fast and outcompetes other comparable commonly used approaches, thus providing a new tool for the thorough analysis of complex datasets without the need for massive computational infrastructure. |
dc.format.extent | 11 p. |
dc.language.iso | eng |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària |
dc.subject.other | t-cell responses |
dc.subject.other | regression |
dc.subject.other | performance |
dc.subject.other | markers |
dc.subject.other | immune |
dc.subject.other | models |
dc.subject.other | genome |
dc.title | FARMS: a new algorithm for variable selection |
dc.type | Article |
dc.contributor.group | Universitat Politècnica de Catalunya. GRBIO - Grup de Recerca en Bioestadística i Bioinformàtica |
dc.identifier.doi | 10.1155/2015/319797 |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.ams | Classificació AMS::62 Statistics |
dc.relation.publisherversion | http://www.hindawi.com/journals/bmri/2015/319797/ |
dc.rights.access | Open Access |
local.identifier.drac | 16844956 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//MTM2012-38067-C02-01/ES/METODOS AVANZADOS EN ESTUDIOS DE SEGUIMIENTO: DISEÑO DE ENSAYOS CLINICOS, DATOS LONGITUDINALES Y CENSURA EN UN INTERVALO/ |
dc.relation.projectid | info:eu-repo/grantAgreement/AGAUR/PRI2010-2013/2014SGR464 |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/241904/EU/Cutaneous and Mucosal HIV Vaccination/CUT'HIVAC |
local.citation.author | Pérez, S.; Gomez, G.; Brander, C. |
local.citation.publicationName | Biomed Research International |
local.citation.volume | 2015 |
local.citation.number | ID 319797 |
local.citation.startingPage | 1 |
local.citation.endingPage | 11 |
dc.identifier.pmid | 26273608 |
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