aCGH-MAS: Analysis of aCGH by means of Multiagent System
There are currently different techniques, such as CGH arrays, to study genetic variations in patients. CGH arrays analyze gains and losses in different regions in the chromosome. Regions with gains or losses in pathologies are important for selecting relevant genes or CNVs (copy-number variations) a...
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doaj-d8f297978f154743bee6f2a54edc44322020-11-24T21:28:20ZengHindawi LimitedBioMed Research International2314-61332314-61412015-01-01201510.1155/2015/194624194624aCGH-MAS: Analysis of aCGH by means of Multiagent SystemJuan F. De Paz0Rocío Benito1Javier Bajo2Ana Eugenia Rodríguez3María Abáigar4Biomedical Research Institute of Salamanca, BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, SpainIBMCC, Cancer Research Center, University of Salamanca-CSIC, 37007 Salamanca, SpainDepartment of Artificial Intelligence, Technical University of Madrid, Campus de Montegancedo, s/n Boadilla del Monte, 28660 Madrid, SpainIBMCC, Cancer Research Center, University of Salamanca-CSIC, 37007 Salamanca, SpainIBMCC, Cancer Research Center, University of Salamanca-CSIC, 37007 Salamanca, SpainThere are currently different techniques, such as CGH arrays, to study genetic variations in patients. CGH arrays analyze gains and losses in different 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 different databases and it would be of interest to incorporate information of different sources to extract relevant information. This 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. The agent roles integrate statistical techniques to select relevant variations and visualization techniques for the interpretation of the final results and to extract relevant information from different sources of information by applying a CBR system.http://dx.doi.org/10.1155/2015/194624 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Juan F. De Paz Rocío Benito Javier Bajo Ana Eugenia Rodríguez María Abáigar |
spellingShingle |
Juan F. De Paz Rocío Benito Javier Bajo Ana Eugenia Rodríguez María Abáigar aCGH-MAS: Analysis of aCGH by means of Multiagent System BioMed Research International |
author_facet |
Juan F. De Paz Rocío Benito Javier Bajo Ana Eugenia Rodríguez María Abáigar |
author_sort |
Juan F. De Paz |
title |
aCGH-MAS: Analysis of aCGH by means of Multiagent System |
title_short |
aCGH-MAS: Analysis of aCGH by means of Multiagent System |
title_full |
aCGH-MAS: Analysis of aCGH by means of Multiagent System |
title_fullStr |
aCGH-MAS: Analysis of aCGH by means of Multiagent System |
title_full_unstemmed |
aCGH-MAS: Analysis of aCGH by means of Multiagent System |
title_sort |
acgh-mas: analysis of acgh by means of multiagent system |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2015-01-01 |
description |
There are currently different techniques, such as CGH arrays, to study genetic variations in patients. CGH arrays analyze gains and losses in different 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 different databases and it would be of interest to incorporate information of different sources to extract relevant information. This 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. The agent roles integrate statistical techniques to select relevant variations and visualization techniques for the interpretation of the final results and to extract relevant information from different sources of information by applying a CBR system. |
url |
http://dx.doi.org/10.1155/2015/194624 |
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1725971025005379584 |