Cited By
View all- Latif ABrunner E(2016)A genetic algorithm for designing microarray experimentsComputational Statistics10.1007/s00180-015-0618-231:2(409-424)Online publication date: 1-Jun-2016
Optimization of cDNA Microarray Experimental Designs Using an Evolutionary Algorithm
Pages 630 - 638
Abstract
The cDNA microarray is an important tool for generating large datasets of gene expression measurements.An efficient design is critical to ensure that the experiment will be able to address relevant biologicalquestions. Microarray experimental design can be treated as a multicriterion optimization problem. For thisclass of problems evolutionary algorithms (EAs) are well suited, as they can search the solution space andevolve a design that optimizes the parameters of interest based on their relative value to the researcher undera given set of constraints. This paper introduces the use of EAs for optimization of experimental designs ofspotted microarrays using a weighted objective function. The EA and the various criteria relevant to designoptimization are discussed. Evolved designs are compared with designs obtained through exhaustive searchwith results suggesting that the EA can find just as efficient optimal or near-optimal designs within atractable timeframe.
References
[1]
M. Schena, D. Shalon, R.W. Davis, and P.O. Brown, "Quantitative Monitoring of Gene-Expression Patterns with a Complementary-DNA Microarray," Science, vol. 270, no. 5235, pp. 467-470, 1995.
[2]
DNA Microarrays, M. Schena, ed. Oxford Univ. Press, 1999.
[3]
M. Schena, Microarray Analysis. John Wiley & Sons, 2002.
[4]
R. Simon, M.D. Radmacher, and K. Dobbin, "Design of Studies Using DNA Microarrays," Genetic Epidemiology, vol. 23, no. 1, pp. 21-36, 2002.
[5]
S. Draghici, A. Kuklin, B. Hoff, and S. Shams, "Experimental Design, Analysis of Variance and Slide Quality Assessment in Gene Expression Arrays," Current Opinion in Drug Discovery and Development, vol. 4, no. 3, pp. 332-337, 2001.
[6]
M.K. Kerr and G.A. Churchill, "Experimental Design for Gene Expression Microarrays," Biostatistics, vol. 2, pp. 183-201, 2001.
[7]
G.A. Churchill, "Fundamentals of Experimental Design for cDNA Microarrays," Nature Genetics, vol. 32, pp. 490-495, 2002.
[8]
K. Dobbin and R. Simon, "Comparison of Microarray Designs for Class Comparison and Class Discovery," Bioinformatics, vol. 18, no. 11, pp. 1438- 1445, 2002.
[9]
R. Simon, M.D. Radmacher, and K. Dobbin, "Design of Studies Using DNA Microarrays," Genetic Epidemiology, vol. 23, no. 1, pp. 21-36, 2002.
[10]
Y.H. Yang and T. Speed, "Design Issues for cDNA Microarray Experiments," Nature Rev. Genetics, vol. 3, no. 8, pp. 579-588, 2002.
[11]
R.M. Simon and K. Dobbin, "Experimental Design of DNA Microarray Experiments," Biotechniques, vol. 34, pp. S16-S21, 2003.
[12]
K. Dobbin, J.H. Shih, and R. Simon, "Statistical Design of Reverse Dye Microarrays," Bioinformatics, vol. 19, no. 7, pp. 803-810, 2003.
[13]
G.F.V. Glonek and P.J. Solomon, "Factorial and Time Course Designs for cDNA Microarray Experiments," Biostatistics, vol. 5, no. 1, pp. 89-111, 2004.
[14]
V. Vinciotti, R. Khanin, D. D'Alimonte, X. Liu, N. Cattini, G. Hotchkiss, G. Bucca, O. de Jesus, J. Rasaiyaah, C.P. Smith, P. Kellam, and E. Wit, "An Experimental Evaluation of a Loop Versus a Reference Design for Two-Channel Microarrays," Bioinformatics, vol. 21, no. 4, pp. 492-501, 2005.
[15]
Evolutionary Computation 1: Basic Algorithms and Operators, T. Bäck, D.B. Fogel, and T. Michalewicz., eds. Inst. Physics Publishing, 2000.
[16]
Evolutionary Computation 2: Advanced Algorithms and Operators, T. Bäck, D.B. Fogel, and T. Michalewicz, eds. Inst. Physics Publishing, 2000.
[17]
Handbook of Evolutionary Computation, T. Bäck, ed. Inst. Physics Publishing, 2003.
[18]
R.A. Fisher, The Design of Experiments. Hafner, 1971.
[19]
S. Draghici, Data Analysis Tools for DNA Microarrays. Chapman & Hall/ CRC, 2003.
[20]
J. Schuchhardt, D. Beule, A. Malik, E. Wolski, H. Eickhoff, H. Lehrach, and H. Herzel, "Normalization Strategies for cDNA Microarrays," Nucleic Acids Research, vol. 28, no. 10, p. E47, 2000.
[21]
J. Quackenbush, "Microarray Data Normalization and Transformation," Nature Genetics, vol. 32, pp. 496-501, 2002.
[22]
Y.F. Leung and D. Cavalieri, "Fundamentals of cDNA Microarray Data Analysis," Trends in Genetics, vol. 19, no. 11, pp. 649-659, 2003.
[23]
G.K. Smyth and T. Speed, "Normalization of cDNA Microarray Data," Methods, vol. 31, no. 4, pp. 265-273, 2003.
[24]
M.K. Kerr, "Experimental Design to Make the Most of Microarray Studies," Methods in Molecular Biology, vol. 224, pp. 137-147, 2003.
[25]
X. Yang, "Optimal Design of Single Factor cDNA Microarray Experiments and Mixed Models for Gene Expression Data," Dept. of Statistics, Virginia Polytechnic Inst. and State Univ., 2003.
[26]
E. Wit, A. Nobile, and R. Khanin, "Near-Optimal Designs for Dual Channel Microarray Studies," Applied Statistics, vol. 54, no. 5, pp. 817-830, 2005.
[27]
Evolutionary Computation in Bioinformatics, D.B. Fogel and D.W. Corne, eds. Morgan Kaufmann, 2003.
[28]
T. Michalewicz and D.B. Fogel, How to Solve It: Modern Heuristics. Springer, 2000.
[29]
E. Zitzler, K. Deb, and L. Thiele, "Comparison of Multiobjective Evolutionary Algorithms: Empirical Results," Evolutionary Computation, vol. 8, no. 2, pp. 173-195, 2000.
[30]
D.A. Van Veldhuizen and G.B. Lamont, "Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art," Evolutionary Computation, vol. 8, no. 2, pp. 125-147, 2000.
[31]
B.P. Kinghorn, "The Tactical Approach to Implementing Breeding Programs," Animal Breeding--Use of New Technologies, B.P. Kinghorn, J.H.J. Van der Werf, and M. Ryan. Sydney, Univ. of Sydney Veterinary Post Graduate Foundation, 2000.
[32]
R.W. Floyd, "Algorithm 97: Shortest Path," Comm. ACM, vol. 5, no. 6, p. 345, 1962.
Index Terms
- Optimization of cDNA Microarray Experimental Designs Using an Evolutionary Algorithm
Recommendations
Mining pathway signatures from microarray data and relevant biological knowledge
High-throughput technologies such as DNA microarray are in the process of revolutionising the way modern biological research is being done. Bioinformatics tools are becoming increasingly important to assist biomedical scientists in their quest in ...
A probabilistic approach for automated discovery of perturbed genes using expression data from microarray or RNA-Seq
BackgroundIn complex diseases, alterations of multiple molecular and cellular components in response to perturbations are indicative of disease physiology. While expression level of genes from high-throughput analysis can vary among patients, the common ...
Prediction of Gene Regulatory Networks Using Differential Expression of cDNA Microarray Data
ISMSE '04: Proceedings of the IEEE Sixth International Symposium on Multimedia Software EngineeringSeveral of ten thousands functional genes control the growth, genetics, and behavior of living organisms by regulating different gene expressions. The genes in a normal cell control the process of cell growth, differentiation, reproduction, and ...
Comments
Information & Contributors
Information
Published In
Publisher
IEEE Computer Society Press
Washington, DC, United States
Publication History
Published: 01 October 2008
Published in TCBB Volume 5, Issue 4
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 169Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)0
Reflects downloads up to 01 Mar 2025
Other Metrics
Citations
Cited By
View all- Latif ABrunner E(2016)A genetic algorithm for designing microarray experimentsComputational Statistics10.1007/s00180-015-0618-231:2(409-424)Online publication date: 1-Jun-2016
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in