Relevance, redundancy and differential prioritization in feature selection for multiclass gene expression data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3745 LNBI, Issue , 2005, Pages 367-378

  Ooi, Chia Huey ^a   Chetty, Madhu ^a   Teng, Shyh Wei ^a  

^a UNIVERSITY OF BALLARAT   (Australia)

Author keywords

Feature selection; Microarray data analysis; Molecular classification

Indexed keywords

BIOMEDICAL ENGINEERING; CLASSIFICATION (OF INFORMATION); ESTIMATION; POLARIZATION; REDUNDANCY;

FEATURE SELECTION; LEAVE-ONE-OUT-CROSS-VALIDATION; MICROARRAY DATA ANALYSIS; MOLECULAR CLASSIFICATION;

GENES;

EID: 33745296717     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11573067_37     Document Type: Conference Paper

References (18)

1. Li, T., Zhang, C., Ogihara, M.: A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20 (2004) 2429-2437
2. Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C.H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J.P., Poggio, T., Gerald, W., Loda, M., Lander, E.S., Golub, T.R.: Multi-class cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. 98 (2001) 15149-15154
3. Chai, H., Domeniconi, C.: An Evaluation of Gene Selection Methods for Multi-class Microarray Data Classification. In: Proc. 2nd European Workshop on Data Mining and Text Mining in Bioinformatics (2004) 3-10
4. Ding, C., Peng, H.: Minimum Redundancy Feature Selection from Microarray Gene Expression Data. In: Proc. 2nd IEEE Computational Systems Bioinformatics Conference. IEEE Computer Society (2003) 523-529
5. Yu, L., Liu, H.: Efficiently Handling Feature Redundancy in High-Dimensional Data. In: Domingos, P., Faloutsos, C., Senator, T., Kargupta, H., Getoor, L. (eds.): Proc. 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, New York (2003) 685-690
6. stAustralasian Computer Science Conference. Springer, Singapore (1998) 181-191
7. Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3 (2003) 1157-1182
8. Knijnenburg, T.A.: Selecting relevant and non-redundant features in microarray classification applications. M.Sc. Thesis. Delft University of Technology. http://ict.ewi.tudelft.nl/pub/marcel/Knij05b.pdf (2004)
9. Ooi, C.H., Chetty, M., Gondal, I.: The role of feature redundancy in tumor classification. In: He, M., Narasimhan, G., Petoukhov, S. (eds.): Proc. International Conference on Bioinformatics and its Applications (ICBA'04). World Scientific Publishing (2004) 197-208
10. Dudoit, S., Fridlyand, J., Speed, T.: Comparison of discrimination methods for the classification of tumors using gene expression data. JASA 97 (2002) 77-87
11. Ambroise, C., McLachlan, G. J.: Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. 99 (2002) 6562-6566
12. th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia. Springer (2003) 108-120
13. Ross, D.T., Scherf, U., Eisen, M.B., Perou, C.M., Spellman, P., Iyer, V., Jeffrey, S.S., Van de Rijn, M., Waltham, M., Pergamenschikov, A., Lee, J.C.F., Lashkari, D., Shalon, D., Myers, T.G., Weinstein, J.N., Botstein, D., Brown, P.O.: Systematic variation in gene expression patterns in human cancer cell lines, Nature Genetics 24(3) (2000) 227-234
14. Bhattacharjee, A., Richards, W.G., Staunton, J., Li, C., Monti, S., Vasa, P., Ladd, C., Beheshti, J., Bueno, R., Gillette, M., Loda, M., Weber, G., Mark, E.J., Lander, E.S., Wong, W., Johnson, B.E., Golub, T.R., Sugarbaker, D.J., Meyerson, M.: Classification of Human Lung Carcinomas by mRNA Expression Profiling Reveals Distinct Adenocarcinoma Subclasses. Proc. Natl. Acad. Sci. 98 (2001) 13790-13795
15. Armstrong, S.A., Staunton, J.E., Silverman, L.B., Pieters, R., den Boer, M.L., Minden, M.D., Sallan, S.E., Lander, E.S., Golub, T.R., Korsmeyer, S.J.: MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nature Genetics 30 (2002) 41-47
16. Golub, T.R., 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) 531-537
17. Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large Margin DAGs for Multiclass Classification. Advances in Neural Information Processing Systems 12 (2000) 547-553
18. Linder, R., Dew, D., Sudhoff, H., Theegarten D., Remberger, K., Poppl, S.J., Wagner, M.: The 'subsequent artificial neural network' (SANN) approach might bring more classificatory power to ANN-based DNA microarray analyses. Bioinformatics 20 (2004) 3544-3552