Classification consistency analysis for bootstrapping gene selection
- 103 Downloads
Consistency modelling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of operations such as classification, clustering, or gene selection on a training set is often found to be very different from the same operations on a testing set, presenting a serious consistency problem. In practice, the inconsistency of microarray datasets prevents many typical gene selection methods working properly for cancer diagnosis and prognosis. In an attempt to deal with this problem, this paper proposes a new concept of classification consistency and applies it for microarray gene selection problem using a bootstrapping approach, with encouraging results.
The research presented in the paper was partially funded by the New Zealand Foundation for Research, Science and Technology under the grant: NERF/AUTX02-01.
- 1.Ding C, Peng H (2003) Minimum Redundancy Feature Selection for Gene Expression Data. In: Paper presented at the Proc. IEEE Computer Society Bioinformatics Conference (CSB 2003), StanfordGoogle Scholar
- 3.Jaeger J, Sengupta R et al (2003) Improved gene selection for classification of microarrays. In: Paper presented at the Pacific Symposium on BiocomputingGoogle Scholar
- 6.Duch W, Biesiada J (2006) Margin based feature selection filters for microarray gene expression data. Int J Inform Technol Intell Comput 1:9–33Google Scholar
- 11.Kauai H, Kasabov N, Middlemiss M et al (2003) A generic connectionist-based method for on-line feature selection and modelling with a case study of gene expression data analysis. In: Paper presented at the Conferences in Research and Practice in Information Technology Series: proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003, vol 19, Adelaide, AustraliaGoogle Scholar
- 12.Wang Z, Palade V, Xu Y (2006) Neuro-Fuzzy ensemble approach for microarray cancer gene expression data analysis. In: Proceedings of 2006 international symposium on evolving fuzzy systems, pp 241–246Google Scholar
- 13.Wolf L, Shashua A et al (2004) Selecting relevant genes with a spectral approach (No. CBCL Paper No.238). Massachusetts Institute of Technology, CambridgeGoogle Scholar
- 19.Mukherjee S, Roberts SJ (2004) Probabilistic consistency analysis for gene selection. Paper presented at the CSB, StanfordGoogle Scholar
- 26.Gordon GJ, Jensen R et al (2002) Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Research 62:4963–4967Google Scholar
- 28.Kohavi R (1995) A study of crossvalidation and bootstrap for accuracy estimation and model selection. In: Paper presented at the international joint conference on artificial intelligence (IJCAI), MontrealGoogle Scholar
- 32.Kawasaki ES (2006) The end of the microarray tower of babel: will universal standards lead the way? J Biomol Tech 17:200–206Google Scholar
- 33.Pham TD, Wells C et al (2006) Analysis of microarray gene expression data. Curr Bioinform 1:37–53Google Scholar
- 34.Asyali MH, Colak D et al (2006) Gene expression profile classification: a review. Curr Bioinform 1:55–73Google Scholar