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BioMed Research International
Volume 2015 (2015), Article ID 319797, 11 pages
Research Article

FARMS: A New Algorithm for Variable Selection

1AIDS Research Institute IrsiCaixa-HIVACAT, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, 08916 Badalona, Spain
2Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
3Institució Catalana de Recerca Avançada (ICREA), 08010 Barcelona, Spain
4University of Vic and Central Catalonia (UVIC-UCC), 08500 Vic, Spain

Received 16 January 2015; Revised 13 March 2015; Accepted 13 March 2015

Academic Editor: Junwen Wang

Copyright © 2015 Susana Perez-Alvarez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.