Integration of clinical and microarray data with kernel methods

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn , Issue , 2007, Pages 5411-5415

  Daemen, Anneleen ^a   Gevaert, Olivier ^a   De Moor, Bart ^a  

^a Katholieke Universiteit Leuven   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; DISEASES; MICROARRAYS; SENSITIVITY ANALYSIS; SUPPORT VECTOR MACHINES; TUMORS;

CLINICAL MANAGEMENT; PROGNOSTIC CATEGORIES;

ONCOLOGY;

TUMOR MARKER; TUMOR PROTEIN;

ALGORITHM; ARTICLE; BREAST TUMOR; CLINICAL MEDICINE; COMPUTER ASSISTED DIAGNOSIS; DECISION SUPPORT SYSTEM; DNA MICROARRAY; EVALUATION STUDY; FEMALE; HUMAN; METABOLISM; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SYSTEM ANALYSIS;

ALGORITHMS; BREAST NEOPLASMS; CLINICAL MEDICINE; DECISION SUPPORT SYSTEMS, CLINICAL; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; NEOPLASM PROTEINS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SYSTEMS INTEGRATION; TUMOR MARKERS, BIOLOGICAL;

EID: 57649188067     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2007.4353566     Document Type: Conference Paper

References (15)

1. Balslev I, Axelsson C, Zedeler K, Rasmussen B, Carstensen B, Mouridsen H (1994). The Nottingham Prognostic Index applied to 9.149 patients from the studies of the Danish Breast Cancer Cooperative Group (DBCG). Breast Cancer Res Treat, 32, 281-90.
2. Chang H, Nuyten D, Sneddon J, Hastie T, Tibshirani R, Sorlie T, Dai H, He Y, van't Veer L, Bartelink H, van de Rijn M, Brown P, van de Vijver M (2005). Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc. Natl Acad. Sci. USA
3. Eifel P, Axelson J, Costa J, et al. (2001). National Institutes of Health Consensus development conference statement: adjuvant therapy for breast cancer. J Natl Cancer Inst, 93, 979-89.
4. Galea M, Blarney R, Elston C, Ellis I (1992). The Nottingham Prognostic Index in primary breast cancer. Breast Cancer Res Treat, 22, 207-19.
5. Goldhirsch A, Glick J, Gelber R, Coates A, Senn H (2001). Meeting highlights: International consensus panel on the treatment of primary breast cancer: Seventh international conference on adjuvant therapy of primary breast cancer. J Clin Oncol, 19, 3817-27.
6. Lanckriet G, De Bie T, Cristianini N, Jordan M, Noble W (2004). A statistical framework for genomic data fusion. Bioinformatics, 20(16), 2626-35.
7. Pavlidis P, Weston J, Cai J, Grundy W (2001). Gene functional, classification, from heterogeneous data. Proceedings of the Fifth Annual International Conference on Computational Molecular Biology, 242-48.
8. Pochet N, De Smet F, Suykens J, De Moor B (2004). Systematic benchmarking of microarray data classification: assessing the role of nonlinearity and dimensionality reduction. Bioinformatics, 20(17), 3185-95.
9. Scholkopf B, Tsuda K, Vert J-P. Kernel methods in computational biology. MIT Press.
10. Suykens J, Vandewalle, J (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293-300.
11. Suykens J, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J (2002). Least Squares Support Vector Machines. World Scientific Publishing Co., Pte Ltd. (Singapore).
12. Todd J, Dowle C, Williams M, Elston C, Ellis I, Hinton C, Blarney R, Haybittle J (1987). Confirmation of a prognostic index in primary breast cancer. Br J Cancer, 56, 489-92.
13. Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman R (2001). Missing value estimation methods for DNA microarrays. Bioinformatics, 17(6), 520-25.
14. van de Vijver M, He Y, van't Veer L, Dai H, Hart A, Voskuil D, Schreiber G, Peterse J, Roberts C, Marton M, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers E, Friend S, Bernards R (2002). A geneexpression signature as a predictor of survival in breast cancer. N Engl J Med, 347(25), 1999-2009.
15. Vapnik V (1998). Statistical Learning Theory. Adaptive and learning systems for signal processing, communications and control Wiley, New York.