Molecular classification of multiple tumor types

Bioinformatics

Volumn 17, Issue , 2001, Pages S316-S322

  Yeang, Chen Hsiang ^a   Ramaswamy, Sridhar ^a   Tamayo, Pablo ^a   Mukherjee, Sayan ^a   Rifkin, Ryan M ^a   Angelo, Michael ^a   Reich, Michael ^a   Lander, Eric ^a   Mesirov, Jill ^a   Golub, Todd ^a  

^a WHITEHEAD INSTITUTE FOR BIOMEDICAL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGY; CLASSIFICATION; CONFIDENCE INTERVAL; GENE EXPRESSION PROFILING; GENETIC DATABASE; GENETICS; HUMAN; NEOPLASM; STATISTICS; VALIDATION STUDY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; CONFIDENCE INTERVALS; DATABASES, GENETIC; GENE EXPRESSION PROFILING; HUMANS; NEOPLASMS;

EID: 0000060617     PISSN: 13674803     EISSN: None     Source Type: Journal    
DOI: 10.1093/bioinformatics/17.suppl_1.S316     Document Type: Article

References (13)

1. Alizadeh, A., Eisen, M., Davis, R., Ma, C., Lossos, I., Rosenwald, A., Boldrick, J., Sabet, H., Tran, T., Yu, X., Powell, J., Yang, L., Marti, G., Moore, T., Hudson, J., Lu, L., Lewis, D., Tibshirani, R., Sherlock, G., Chan, W., aan D.D. Weisenburger, T. G., Armitage, J., Warnke, R., Levy, R., Wilson, W., Grever, M. R., Bvrd, J., Bostein, D., Brown, P. & Staudt, L. (2000). Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature, 403, 503-511.
2. Allwein, E., Shapire, R. & Singer, Y. (2000). Reducing multiclass to binary: a unifying approach for margin classifiers. In Proceeding of international conference on machine learning.
3. Ben-Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M. & Yakhini, Z. (2000). Tissue classification with gene expression profiles. In Proceeding of the fourth annual international conference on computational molecular biology, pp. 54-64.
4. Breiman, L. (1984). Classification and regression trees. Wadsworth international group.
5. Crammer, K. & Singer, Y. (2000). On the learnability and design of output codes for multiclass problems. In Proceeding of ACM conference on computational learning theory.
6. Dietterich, T. & Bakiri, G. (1991). Error-correcting output codes: a general method for improving multiclass inductive programs. In Proceeding of the eighth national conference on artificial intelligence, pp. 572-577.
7. Dietterich, T. & Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes. Journal of artificial intelligence research, 2, 263-286.
8. Duda, R. (2001). Pattern classification. Wiley.
9. Golub, T., Slonim, D., Tamayo, P., Huard, C., Gassenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J., Caligiuri, M., Bloomfield, C. & Lander, E. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
10. Hanahan, D. & Weinberg, R. (2000). The hallmark of cancer. Cell, 100, 57-71.
11. Minsky, M. (1969). Perceptrons: an introduction to computational geometry. MIT Press.
12. Slonim, D., Tamayo, P., Mesirov, J., Golub, T. & Lander, E. (2000). Class prediction and discovery using gene expression data. In Proceeding of the fourth annual international conference on computational molecular biology, pp. 263-272.
13. Vapnik, V. (1998). Statistical learning theory. Wiley.