Abstract
Classifiers, as one of the important tools of analyzing gene expression data in the post-genomic epoch, have been used widely in the classification of different cancer types in the past few years. Although most existing classifiers have high classification accuracy, the process of classification is a black box and they can not give biologists more information and interpretable results of classification. In this paper, we propose a novel visualization cancer classification method. Besides offering high classification accuracy, the method can help us identify complex disease-related genes and assess gene expression variation during the process of classification. The results of classification are natural and interpretable and the process of classification is visible. To evaluate the performance of the method we have applied the proposed method to three public data sets. The experimental results demonstrate that the approach is feasible and useful.
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References
Brazma, A., Vilo, J.: Minireview: Gene expression data analysis. Federation of European Biochemical Societies 480, 17–24 (2000)
Welsh, J.B., Zarrinkar, P.P., et al.: Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. Proc.Natl Acad. Sci.USA 98, 1176–1181 (2001)
Eisen, M.B., Spellman, P.T., et al.: Cluster analysis and display of genome-wide expression patterns. Proc.Natl Acad. Sci. USA 95, 14863–14868 (1998)
Alizadeh, A.A., Eisen, M.B., et al.: Distinct types of diffuse large B-Cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)
Alter, O., Brown, P.O., et al.: Singular value decomposition for genome-wide expression data processing and modeling. Proc.Natl Acad. Sci. USA 97, 10101–10106 (2000)
Holter, N.S., Maritan, A., et al.: Dynamic modeling of gene Expression Data. Proc.Natl Acad. Sci. USA 98, 1693–1698 (2001)
Wen, X., Fuhrman, S., et al.: Large-scale temporal gene expression mapping of central nervous system development. Proc.Natl Acad. Sci. USA 95, 334–339 (1998)
Zhang, H., Yu, C.-Y., et al.: Recursive partitioning for tumor classification with gene expression microarray data. Proc.Natl Acad. Sci. USA 98, 6730–6735 (2001)
Golub, T.R., Slonim, D.K., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Yeung, K.Y., Haynor, D.R., et al.: Validating clustering for gene expression data. Bioinformatics 17, 309–318 (2001)
Holter, N.S., Mitra, M., et al.: Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc.Natl Acad. Sci. USA 97, 8409–8414 (2000)
Tamayo, P., Slonim, D., et al.: Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc.Natl Acad. Sci.USA 96, 2907–2912 (1999)
Furey, T.S., Cristianini, N., et al.: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906–914 (2002)
Ewing, R.M., Cherry, J.M.: Visualization of expression clusters using Sammon’s no-linear mapping. Bioinformatics 17, 658–659 (2001)
Ben-Dor, A., Bruhn, L., et al.: Tissue classification with gene expression profiles. In: Proceedings of the Fourth Annual International Conference on Computational Molecular Biology, Tokyo, Japan (2000)
Model, F., Adorjan, P., et al.: Feature selection for DNA methylation based cancer classification. Bioinformatics 17, S157–S164 (2001)
Ding, C.H.Q.: Analysis of gene expression profiles: class discovery and leaf ordering. In: RECOMB 2002, pp. 127–136 (2002)
Wuju, L., Momiao, X.: Tclass: tumor classification system based on gene expression profile. Bioinformatics 18, 325–326 (2002)
Li, L., Clarice, R., et al.: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17, 1131–1142 (2001)
Alon, U., Barkai, N., et al.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed Oligonucleotide arrays. Proc.Natl Acad. Sci. USA 96, 6745–6750 (1999)
Armstrong, S.A., Staunton, J.E., et al.: MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nat Genet 30, 41–47 (2002)
Joachims, T.: Making large-Scale SVM Learning Practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, MIT-Press, Cambridge (1999)
Wang, X., Lee, G., et al.: A member of the LIM/double-finger family displaying coordinate serum induction with c-myc. J Biol Chem. 267, 9176–9184 (1992)
Heikkila, R., Schwab, G., et al.: A c-myc antisense oligodeoxynucleotide inhibits entry into S phase but not progress from G0 to G1. Nature 328, 445–449 (1987)
Xia, L., Rao, S., et al.: Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling. Nucleic Acids Res. 32, 2685–2694 (2004)
Su, Y., Murali, T.M., et al.: RankGene: Identification of diagnostic genes based on expression data. Bioinformatics 19, 1578–1579 (2003)
Gutgemann, A., Golob, M., et al.: Isolation of invasion-associated cDNAs in melanoma. Arch Dermatol Res. 293, 283–290 (2001)
Fox, S.H., Whalen, G.F., et al.: Angiogenesis in normal tissue adjacent to colon cancer. J. Surg. Oncol. 69, 230–234 (1998)
Toshina, K., Hirata, I., et al.: Enprostil, a prostaglandin-E(2) analogue, inhibits interleukin-8 production of human colonic epithelial cell lines. Scand. J. Immunol. 52, 570–575 (2000)
Inoue, K., Perrotte, P., et al.: Gene therapy of human bladder cancer with adenovirus-mediated antisense basic fibroblast growth factor. Clin. Cancer Res. 6, 4422–4431 (2000)
Kowalski, J., Denhardt, D.T.: Regulation of the mRNA for monocyte-derived neutrophil-activating peptide in differentiating HL60 promyelocytes. Mol. Cell Biol. 9, 1946–1957 (1989)
Thommes, P., Fett, R., et al.: Properties of the nuclear P1 protein, a mammalian homologue of the yeast Mcm3 replication protein. Nucleic Acids Res. 20, 1069–1074 (1992)
Jindal, S., Dudani, A.K., et al.: Primary structure of a human mitochondrial protein homologous to the bacteriacterial and plant chaperonins and to the 65-Kilodalton mycobacterial antigen. Mol. Cell Biol. 9, 1989–2279 (1989)
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Li, J., Tang, X.L., Li, X. (2005). A Novel Visualization Classifier and Its Applications. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_157
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DOI: https://doi.org/10.1007/11540007_157
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
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