Research Article aCGH-MAS: Analysis of aCGH by means of Multiagent System Juan F. De Paz, 1 Rocío Benito, 2 Javier Bajo, 3 Ana Eugenia Rodríguez, 2 and María Abáigar 2 1 Biomedical Research Institute of Salamanca, BISITE Research Group, University of Salamanca, Edifcio I+D+i, 37008 Salamanca, Spain 2 IBMCC, Cancer Research Center, University of Salamanca-CSIC, 37007 Salamanca, Spain 3 Department of Artifcial Intelligence, Technical University of Madrid, Campus de Montegancedo, s/n Boadilla del Monte, 28660 Madrid, Spain Correspondence should be addressed to Juan F. De Paz; fcofds@usal.es Received 21 August 2014; Revised 31 October 2014; Accepted 17 November 2014 Academic Editor: Juan M. Corchado Copyright © 2015 Juan F. De Paz et al. Tis 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. Tere are currently diferent techniques, such as CGH arrays, to study genetic variations in patients. CGH arrays analyze gains and losses in diferent regions in the chromosome. Regions with gains or losses in pathologies are important for selecting relevant genes or CNVs (copy-number variations) associated with the variations detected within chromosomes. Information corresponding to mutations, genes, proteins, variations, CNVs, and diseases can be found in diferent databases and it would be of interest to incorporate information of diferent sources to extract relevant information. Tis work proposes a multiagent system to manage the information of aCGH arrays, with the aim of providing an intuitive and extensible system to analyze and interpret the results. Te agent roles integrate statistical techniques to select relevant variations and visualization techniques for the interpretation of the fnal results and to extract relevant information from diferent sources of information by applying a CBR system. 1. Introduction Tere are various techniques for performing studies on genetic variations in patients, including expression arrays [1, 2], CGH (comparative genomic hybridization) arrays [3], and studies at the genetic sequence level. CGH arrays allow comparing the DNA of a patient with a control DNA and using this information to detect mutations [4, 5] based on gains, losses, and amplifcations [6]. Another kind of microarrays is the expression arrays, which determine the expression of diferent genes with probes. CGH are used to detect regions in the chromosomes with variations in certain pathologies. Tis information is taken into account for sequencing these regions through the use of expression arrays and sequencers [7]. In these studies, the users have to work with a vast amount of information, which implies the development of systems oriented to improve the analysis of the data and to automatically extract information using databases [8]. For this reason, it is necessary to identify the exact location of those interesting genes in CGH arrays before carrying out the sequencing. Tere are currently various tools that provide a visual analysis of the information of aCGH. Tese tools typically represent the information but the interaction with the infor- mation is complex. Te visual analysis is used to represent additional information about relevant regions. Some of these tools can be found in works [9–14]. A visual analysis of these data is normally performed manually [14, 15], which requires the participation of experts to select the relevant information. However,thesetoolslackusabilityandrequiretheuseoftech- niques that facilitate the automatic analysis and extraction of information from diferent sources. For this reason, it is necessary to incorporate a process that helps determine the interesting genes [16], proteins, and relationships to diseases that must be analyzed and understood in a simpler way. Te distributed analysis of CGH data is performed by diferent laboratory personnel, from hybridating the chips to extracting the relevant variations and information associated with the chips. Tis work shows a multiagent system specif- ically designed to analyze CGH data [17]. Te functionality of the multiagent system is divided into layers and roles to carry out the analysis of CGH arrays. Te analysis is Hindawi Publishing Corporation BioMed Research International Volume 2015, Article ID 194624, 12 pages http://dx.doi.org/10.1155/2015/194624