An ensemble machine learning approach to predict survival in breast cancer

International Journal of Computational Biology and Drug Design

Volumn 1, Issue 3, 2008, Pages 275-294

  Djebbari, Amira ^a   Liu, Ziying ^a   Phan, Sieu ^a   Famili, Fazel ^a  

^a NATIONAL RESEARCH COUNCIL CANADA   (Canada)

Author keywords

bioinformatics; breast cancer prognosis; computational biology; data mining; knowledge discovery; machine learning

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGY; BREAST TUMOR; CLASSIFICATION; COMPUTER SIMULATION; DNA MICROARRAY; FEMALE; GENE EXPRESSION PROFILING; GENETIC DATABASE; GENETICS; HUMAN; MORTALITY; PROGNOSIS; STATISTICS; SURVIVAL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BREAST NEOPLASMS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES, GENETIC; FEMALE; GENE EXPRESSION PROFILING; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROGNOSIS; SURVIVAL ANALYSIS;

EID: 77449133004     PISSN: 17560756     EISSN: 17560764     Source Type: Journal    
DOI: 10.1504/IJCBDD.2008.021422     Document Type: Article

References (32)

1. Ahmed, A.A. and Brenton, J.D. (2005) ‘Microarrays and breast cancer clinical studies: forgetting what we have not yet learnt’, Breast Cancer Res., Vol. 7, No. 3, pp.96–99.
2. Bellman, R. (1961) Adaptive Control Processes: A Guided Tour, Princeton University Press, Princeton, NJ.
3. Bloedorn, E., Michalski, R.S. and Wnek, J. (1993) ‘Multistrategy constructive induction: AQ17-MCI’, Proceedings of the 2nd International Workshop on Multi-strategy Learning, Harpers Ferry, WV, Morgan Kaufmann, pp.188–203.
4. Breiman, L. (1996) ‘Bagging predictors’, Machine Learning, Vol. 24, No. 2, pp.123–140.
5. Brenton, J.D., Carey, L.A., Ahmed, A.A. and Caldas, C. (2005) ‘Molecular classification and molecular forecasting of breast cancer: ready for clinical application?’, J. Clin. Oncol, Vol. 23, No. 29, pp.7350–7360.
6. Buyse, M., Loi, S., van’t Veer, L., Viale, G., Delorenzi, M., Glas, A.M., d’Assignies, M.S., Bergh, J., Lidereau, R., Ellis, P., Harris, A., Bogaerts, J., Therasse, P., Floore, A., Amakrane, M., Piette, F., Rutgers, E., Sotiriou, C., Cardoso, F. and Piccart, M.J. (2006) ‘On behalf of the TRANSBIG consortium. validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer’, J. Natl. Cancer. Inst., Vol. 98, No. 17, pp.1183–1192.
7. Dietterich, T.G. (2000) ‘Ensemble methods in machine learning’, in Kittler, J. and Roli, F. (Eds.): 1st Int. Workshop on Multiple Classifier Systems, in Lecture Notes in Computer Science, Vol. 1857, pp.1–15.
8. Drummond, C. and Holte, R.C. (2005) ‘Severe class imbalance: Why better algorithms aren’t the answer’, in Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M. and Torgo, L. (Eds.): ECML 2005, LNCS (LNAI), Vol. 3720, Springer, Heidelberg, pp.539–546.
9. Edén, P., Ritz, C., Rose, C., Fernö, M. and Peterson, C. (2004) ‘Good old clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers’, Eur. J. Cancer, Vol. 40, No. 12, pp.1837–1841.
10. Ein-Dor, L., Kela, I., Getz, G., Givol, D. and Domany, E. (2005) ‘Outcome signature genes in breast cancer: Is there a unique set?’, Bioinformatics, Vol. 21, No. 2, pp.171–178.
11. Fawcett, T. (2003) ROC Graphs: Notes and Practical Considerations for Data Mining Researchers, HP Labs Tech Report, HPL-2003-4. http://www.hpl.hp.com/techreports/2003/HPL-2003-4.pdf
12. Freund, Y. and Schapire, R.E. (1997) ‘Decision-theoretic generalization of on-line learning and an application to boosting’, Journal of Computer and System Sciences, Vol. 55, No. 1, pp.119–139.
13. Fumera, G. and Roli, F. (2005) ‘A theoretical and experimental analysis of linear combiners for multiple classifier systems’, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 6, pp.942–956.
14. Ho, T.K. (1998) ‘Random subspace method for constructing decision forests’, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, pp.832–844.
15. Jacobs, R.A., Jordan, M.I., Nowlan, S.J. and Hinton, G.E. (1991) ‘Adaptive mixtures of local experts’, Neural Computation, Vol. 3, pp.79–87.
16. Kittler, J., Hatef, M., Duin, R.P.W. and Matas, J. (1998) ‘On combining classifiers’, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 20, pp.226–239.
17. Kuncheva, L.I., Bezdek, J.C. and Duin, R. (2001) ‘Decision templates for multiple classifier fusion: an experimental comparison’, Pattern Recognition, Vol. 34, No. 2, pp.299–314.
18. Langley, P., Iba, W. and Thompson, K. (1992) ‘An analysis of Bayesian classifiers’, Proc. 10th National Conf. on Artificial Intelligence, pp.399–406.
19. Naderi, A., Teschendorff, A.E., Barbosa-Morais, N.L., Pinder, S.E., Green, A.R., Powe, D.G., Robertson, J.F., Aparicio, S., Ellis, I.O., Brenton, J.D. and Caldas, C. (2007) ‘A gene-expression signature to predict survival in breast cancer across independent data sets’, Oncogene, Vol. 26, No. 10, pp.1507–1516.
20. Perou, C.M., Sørlie, T., Eisen, M.B., van de Rijn, M., Jeffrey, S.S., Rees, C.A., Pollack, J.R., Ross, D.T., Johnsen, H., Akslen, L.A., Fluge, O., Pergamenschikov, A., Williams, C., Zhu, S.X., Lønning, P.E., Børresen-Dale, A.L., Brown, P.O. and Botstein, D. (2000) ‘Molecular portraits of human breast tumours’, Nature, Vol. 406, No. 6797, pp.747–752.
21. Polikar, R. (2006) ‘Ensemble based systems in decision making’, IEEE Circuits and Systems Mag., Vol. 6, No. 3, pp.21–45.
22. Provost, F.J., Fawcett, T. and Kohavi, R. (1998) ‘The case against accuracy estimation for comparing induction algorithms’, Proceedings of the Fifteenth International Conference on Machine Learning, pp.445–453.
23. Salzberg, S.L. (1997) ‘On comparing classifiers: pitfalls to avoid and a recommended approach’, Data Mining and Knowledge Discovery, Vol. 1, pp.317–327.
24. Schapire, R.E. (1990) ‘The strength of weak learnability’, Machine Learning, Vol. 5, No. 2, pp.197–227.
25. Sørlie, T., Perou, C.M., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M.B., van de Rijn, M., Jeffrey, S.S., Thorsen, T., Quist, H., Matese, J.C., Brown, P.O., Botstein, D., Eystein Lønning, P. and Børresen-Dale, A.L. (2001) ‘Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications’, Proc. Natl. Acad. Sci. U.S.A., Vol. 98, No. 19, pp.10869–10874.
26. Ting, K.M. and Witten, I.H. (1999) ‘Issues in stacked generalization’, Journal of Artificial Intelligence Research, Vol. 10, No. 271, p.189.
27. Van’t Veer, L.J., Dai, H., van de Vijver, M.J., He, Y.D., Hart, A.A., Mao, M., Peterse, H.L., van der Kooy, K., Marton, M.J., Witteveen, A.T., Schreiber, G.J., Kerkhoven, R.M., Roberts, C., Linsley, P.S., Bernards, R. and Friend, S.H. (2002) ‘Gene expression profiling predicts clinical outcome of breast cancer’, Nature, Vol. 415, No. 6871, pp.530–536.
28. van de Vijver, M.J., He, Y.D., van’t Veer, L.J., Dai, H., Hart, A.A., Voskuil, D.W., Schreiber, G.J., Peterse, J.L., Roberts, C., Marton, M.J., Parrish, M., Atsma, D., Witteveen, A., Glas, A., Delahaye, L., van der Velde, T., Bartelink, H., Rodenhuis, S., Rutgers, E.T., Friend, S.H. and Bernards, R. (2002) ‘A gene-expression signature as a predictor of survival in breast cancer’, N Engl. J. Med., Vol. 347, No. 25, pp.1999–2009.
29. Wang, Y., Klijn, J.G., Zhang, Y., Sieuwerts, A.M., Look, M.P., Yang, F., Talantov, D., Timmermans, M., Meijer-van Gelder, M.E., Yu, J., Jatkoe, T., Berns, E.M., Atkins, D. and Foekens, J.A. (2005) ‘Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer’, Lancet, Vol. 365, No. 9460, pp.671–679.
30. Witten, I.H. and Frank, E. (2000) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, San Francisco.
31. Wolpert, D.H. (1992) ‘Stacked generalization’, Neural Networks, Vol. 5, pp.241–259.
32. Wolpert, D.H. and MacReady, W.G. (1997) ‘No free lunch theorems for optimization’, IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1, pp.67–82.