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aCGH-MAS: analysis of aCGH by means of multiagent system.

De Paz JF, Benito R, Bajo J, Rodríguez AE, Abáigar M - Biomed Res Int (2015)

Bottom Line: CGH arrays analyze gains and losses in different regions in the chromosome.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.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Research Institute of Salamanca, BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain.

ABSTRACT
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.

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Automatic selection of segments.
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alg1: Automatic selection of segments.

Mentions: The segments that were considered most relevant for each of the CGH arrays were selected for each pathology. Algorithm 1 displays the selection algorithm for the relevant segments used for the set of arrays and for the individuals with or without a particular pathology, as identified by the groups variable. The algorithm was applied repeatedly for each existing pathology.


aCGH-MAS: analysis of aCGH by means of multiagent system.

De Paz JF, Benito R, Bajo J, Rodríguez AE, Abáigar M - Biomed Res Int (2015)

Automatic selection of segments.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4385609&req=5

alg1: Automatic selection of segments.
Mentions: The segments that were considered most relevant for each of the CGH arrays were selected for each pathology. Algorithm 1 displays the selection algorithm for the relevant segments used for the set of arrays and for the individuals with or without a particular pathology, as identified by the groups variable. The algorithm was applied repeatedly for each existing pathology.

Bottom Line: CGH arrays analyze gains and losses in different regions in the chromosome.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.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Research Institute of Salamanca, BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus