scholarly journals An Evolutionary and Visual Framework for Clustering of DNA Microarray Data

2013 ◽  
Vol 10 (3) ◽  
pp. 51-65
Author(s):  
José A. Castellanos-Garzón ◽  
Fernando Díaz
Keyword(s):  
Genetic Algorithm ◽  
Cluster Analysis ◽  
Dna Microarray ◽  
Microarray Data ◽  
The Other ◽  
Visualization Tool ◽  
Data Set ◽  
Dna Microarray Data ◽  
Cluster Visualization

Summary This paper presents a case study to show the competence of our evolutionary and visual framework for cluster analysis of DNA microarray data. The proposed framework joins a genetic algorithm for hierarchical clustering with a set of visual components of cluster tasks given by a tool. The cluster visualization tool allows us to display different views of clustering results as a means of cluster visual validation. The results of the genetic algorithm for clustering have shown that it can find better solutions than the other methods for the selected data set. Thus, this shows the reliability of the proposed framework.

scholarly journals A visual analytics framework for cluster analysis of DNA microarray data

2013 ◽  
Vol 40 (2) ◽  
pp. 758-774 ◽  
Author(s):  
José A. Castellanos-Garzón ◽  
Carlos Armando García ◽  
Paulo Novais ◽  
Fernando Díaz
Keyword(s):  
Cluster Analysis ◽  
Dna Microarray ◽  
Microarray Data ◽  
Visual Analytics ◽  
Dna Microarray Data

Design and Evaluation of Usable Systems

2002 ◽  
Vol 46 (3) ◽  
pp. 367-371
Author(s):  
John F. McGrew
Keyword(s):  
Genetic Algorithm ◽  
Change Management ◽  
Management System ◽  
Design Method ◽  
The Other ◽  
Design Team ◽  
Style Analysis ◽  
Decision Style ◽  
Parallel Design

This paper discusses a case study of a design and evaluation of a change management system at a large Telecommunications Corporation. The design and evaluation were done using the facilitated genetic algorithm (a parallel design method) and user decision style analysis. During the facilitated genetic algorithm the design team followed the procedure of the genetic algorithm. Usability was evaluated by applying user decision style analysis to the designed system. The design is compared with an existing system and with one designed by an analyst. The change management system designed by the facilitated genetic algorithm took less time to design and decision style analysis indicated it would be easier to use than the other two systems.

An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem

2010 ◽  
Vol 26-28 ◽  
pp. 620-624 ◽  
Author(s):  
Zhan Wei Du ◽  
Yong Jian Yang ◽  
Yong Xiong Sun ◽  
Chi Jun Zhang ◽  
Tuan Liang Li
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Convergence Time ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Research Attention ◽  
Simply Supported ◽  
Source Data

This paper presents a modified Ant Colony Algorithm(ACA) called route-update ant colony algorithm(RUACA). The research attention is focused on improving the computational efficiency in the TSP problem. A new impact factor is introduced and proved to be effective for reducing the convergence time in the RUACA performance. In order to assess the RUACA performance, a simply supported data set of cities, which was taken as the source data in previous research using traditional ACA and genetic algorithm(GA), is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented RUACA has successfully solved the TSP problem. The results of the proposed algorithm are found to be satisfactory.

scholarly journals Extending XMLPipeDB to create GenMAPP‐compatible gene databases for the analysis of DNA microarray data from human pathogens

2010 ◽  
Vol 24 (S1) ◽  
Author(s):  
Kelly C Parks ◽  
Andrew J Hirning ◽  
Kelia McDonald ◽  
John David N. Dionisio ◽  
Kam D Dahlquist
Keyword(s):  
Dna Microarray ◽  
Microarray Data ◽  
Human Pathogens ◽  
Dna Microarray Data
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