aCGH-MAS: Analysis of aCGH by Means of Multi-agent System

There are currently different techniques, such as CGH Arrays, to study genetic variations in patients. Arrays CGH analyze gains and losses in different regions in the chromosomal. Regions with gains or losses in pathologies are important for selecting relevant genes, or CNVs (copy-number variations) associated to 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 multi-agent 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

aCGH-MAS: Analysis of aCGH by Means of Multi-agent System

There are currently different techniques, such as CGH Arrays, to study genetic variations in patients. Arrays CGH analyze gains and losses in different regions in the chromosomal. Regions with gains or losses in pathologies are important for selecting relevant genes, or CNVs (copy-number variations) associated to 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 multi-agent 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

Retreatment Predictions in Odontology by means of CBR Systems

The field of odontology requires an appropriate adjustment of treatments according to the circumstances of each patient. A follow-up treatment for a patient experiencing problems from a previous procedure such as endodontic therapy, for example, may not necessarily preclude the possibility of extraction. It is therefore necessary to investigate new solutions aimed at analyzing data and, with regard to the given values, determine whether dental retreatment is required. In this work, we present a decision support system which applies the case-based reasoning (CBR) paradigm, specifically designed to predict the practicality of performing or not performing a retreatment. Thus, the system uses previous experiences to provide new predictions, which is completely innovative in the field of odontology. The proposed prediction technique includes an innovative combination of methods that minimizes false negatives to the greatest possible extent. False negatives refer to a prediction favoring a retreatment when in fact it would be ineffective. The combination of methods is performed by applying an optimization problem to reduce incorrect classifications and takes into account different parameters, such as precision, recall, and statistical probabilities. The proposed system was tested in a real environment and the results obtained are promising