Gene expression datasets used in biomedical data mining frequently have two characteristics: they have many thousand attributes but only relatively few sample points and the measurements are noisy. In other words, individual expression measurements may be untrustworthy. Gene Feature Ranking (GFR) is a feature selection methodology that addresses these domain specific characteristics by selecting features (i.e. genes) based on two criteria: (i) how well the gene can discriminate between classes of patient and (ii) the trustworthiness of the microarray data associated with the gene. An example from the pediatric cancer domain demonstrates the use of GFR and compares its performance with a feature selection method that does not explicitly address the trustworthiness of the underlying data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hardiman, G.: Microarray technologies - an overview. Pharamacogenomics 3 (2002) 293–297
Schena, M.: Microarray Biochip Technology. BioTechniques Press, Westborough, MA (2000)
Bolstad, B., et al.: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19 (2003) 185–193
Weng, L., Dai, H., Zhan, Y., He, Y., Stepaniants, S.B., Bassett, D.E.: Rosetta error model for gene expression analysis. Bioinformatics 22 (2006) 1111–1121
Seo, J., Gordish-Dressman, H., Hoffman, E.P.: An interactive power analysis tool for microar-ray hypothesis testing and generation. Bioinformatics 22 (2006) 808–814
Tsai, C.A., et al.: Sample size for gene expression microarray experiments. Bioinformatics 21 (2005) 1502–1508
Baldi, P., Hatfield, G.W.: DNA Microarrays and Gene Expression: from experiments to data analysis and modeling. Cambridge University Press (2002)
Golub, T., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286 (1999) 7
Mukherjee, S., Tamayo, P., Slonim, D.K., Verri, A., Golub, T.R., Mesirov, J.P., Poggio, T.: Support vector machine classification of microarray data. AI memo 182. CBCL paper 182. Technical report, MIT (2000) Can be retrieved from ftp://publications.ai.mit.edu.
Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97 (1997) 245–271
Yang, J., Hanavar, V.: Feature subset selection using a genetic algorithm. Technical report, Iowa State University (1997+)
Efron, B., Tibshirani, R., Goss, V., Chu, G.: Microarrays and their use in a comparative experiment. Technical report, Stanford University (2000)
Bellman, R.E.: Adaptive Control Processes. Princeton University Press (1961)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Eleventh International Conference (Machine Learning), Kaufmann Morgan (1994) 121–129
Saeys, Y., Inza, I., et al.: A review of feature selection tecnhiques in bioinformatics. Bioinfor-matics 23 (2007) 2507–2517
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Machine Learning Research (2003) 1157–1182
Wang, X., Ghosh, S., Guo, S.W.: Quantitative quality control in microarray image processing and data acquisition. Nucleic Acids Research 29 (2001) 8
Park, T., Yi, S.G., Lee, S., Lee, J.K.: Diagnostic plots for detecting outlying slides in a cDNA microarray experiment. BioTechniques 38 (2005) 463–471
Yu, Y., Khan, J., et al.: Expression profiling identifies the cytoskeletal organizer ezrin and the developmental homeoprotein six- 1 as key metastatic regulators. Nature Medicine 10 (2004) 175–181
Quinlan, J.R.: Induction of decision trees. Machine Learning 1 (1986) 81–106
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. 2nd edn. Morgan Kaufmann, San Francisco (2005)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, San Francisco, Morgan Kaufmann (1996) 148–156
Dawson, B., Trapp, R.G.: Basic & Clinical Biostatistics. Third edn. Health Professions. McGraw-Hill Higher Education, Singapore (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Franco, U.A., Paul, J.K., Daniel, R.C., Dachuan, G., Simeon, J.S. (2009). Microarray Data Mining: Selecting Trustworthy Genes with Gene Feature Ranking. In: Cao, L., Yu, P.S., Zhang, C., Zhang, H. (eds) Data Mining for Business Applications. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79420-4_11
Download citation
DOI: https://doi.org/10.1007/978-0-387-79420-4_11
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-79419-8
Online ISBN: 978-0-387-79420-4
eBook Packages: Computer ScienceComputer Science (R0)