Specific Tuning Parameter for Directed Random Walk Algorithm Cancer Classification

Accuracy of cancerous gene classification is a central challenge in clinical cancer research. Microarray-based gene biomarkers have proved the performance and its ability over traditional clinical parameters. However, gene biomarkers of an individual are less robustness due to litter reproducibility between different cohorts of patients. Several methods incorporating pathway information such as directed random walk have been proposed to infer the pathway activity. This paper discusses the implementation of group specific tuning parameter in directed random walk algorithm. In this experiment, gene expression data and pathway data are used as input data. Throughout this experiment, more significant pathway activities can be identified which increases the accuracy of cancer classification. The lung cancer gene is used as the experimental dataset, with which, the sDRW is used in determining significant pathways. More risk-active pathways are identified throughout this experiment

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