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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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Related in: MedlinePlus

Clustering review with a bar graph and parallel coordinates.
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fig9: Clustering review with a bar graph and parallel coordinates.

Mentions: Figure 9 shows a representation in parallel coordinates and a bar graph. In the bar graph, one bar represents each individual and is divided into different segments with an amplitude proportional to the width of the segment. The color of the top rectangle represents the type of pathology the patient has. In parallel coordinates, each line is associated with a patient and the color represents the pathology type. Each coordinate represents a segment. If we select the patients from the green category in the stacked bars, we can see how the other bars are deactivated, which indicates that the patients have variations within different ranges; only the patients with variations within the range of the selected patients remain active, which makes it easy to see other similar patients. In the parallel coordinates, the values of each coordinate are adjusted to the maximum and minimum extremes for the selected individuals. The lines for each selected individual are highlighted while those not within the range of maximum and minimum values as established for each coordinate are marked in gray.


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)

Clustering review with a bar graph and parallel coordinates.
© Copyright Policy
Related In: Results  -  Collection

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

fig9: Clustering review with a bar graph and parallel coordinates.
Mentions: Figure 9 shows a representation in parallel coordinates and a bar graph. In the bar graph, one bar represents each individual and is divided into different segments with an amplitude proportional to the width of the segment. The color of the top rectangle represents the type of pathology the patient has. In parallel coordinates, each line is associated with a patient and the color represents the pathology type. Each coordinate represents a segment. If we select the patients from the green category in the stacked bars, we can see how the other bars are deactivated, which indicates that the patients have variations within different ranges; only the patients with variations within the range of the selected patients remain active, which makes it easy to see other similar patients. In the parallel coordinates, the values of each coordinate are adjusted to the maximum and minimum extremes for the selected individuals. The lines for each selected individual are highlighted while those not within the range of maximum and minimum values as established for each coordinate are marked in gray.

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