Predictive modeling of therapy response in multiple sclerosis using gene expression data

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

Volumn , Issue , 2006, Pages 5519-5522

  Mostafavi, Sara ^a   Baranzini, Sergio ^b   Oksernberg, Jorge ^b   Mousavi, Parvin ^a  

^a QUEEN S UNIVERSITY   (Canada)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PHENOTYPIC OUTCOMES; PREDICTIVE MODELING; THERAPY RESPONSES; TRANSCRIPTIONAL PROFILING;

BIOINFORMATICS; COMPUTER SIMULATION; DATA REDUCTION; MATHEMATICAL MODELS; STATISTICAL METHODS;

GENE EXPRESSION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGY; COMPUTER PROGRAM; DNA MICROARRAY; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; HUMAN; METABOLISM; METHODOLOGY; MULTIPLE SCLEROSIS; NORMAL DISTRIBUTION; REPRODUCIBILITY; STATISTICAL ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; HUMANS; MULTIPLE SCLEROSIS; NORMAL DISTRIBUTION; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SOFTWARE;

EID: 34047185294     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2006.259681     Document Type: Conference Paper

References (15)

1. National Multiple Sclerosis Society. http://www.nmss.org.
2. Rio J, et al. (2002) Assessment of different treatment failure criteria in a cohort of relapsing-remitting multiple sclerosis patients treated with Interferon beta: Implications for clinical trials. Ann Neurol 52: 400-406.
3. Baranzini SE, et al.(2005) Transcription-based prediction of response to IFNbeta using supervised computational methods. PLoS Biol. Jan 3, 1:e2.
4. Li, W. and Yang, Y. (2002) How many genes are needed for a discriminant microarray data analysis? Methods of Microarray Data Analysis: Papers from CAMDA'00, eds. SM Lin, KF Johnson (Kluwer Academic), pp. 137-150.
5. Li, T., et al. (2004) A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics, 20, 2429-2437.
6. Blum, A., and Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 245-271.
7. Dudoit, S, Fridlyand, J, and Speed, T. (2002) Comparison of Discriminant Methods for the Classification of Tumors Using Gene Expression Data. Journal of the American Statistical Association: Mar(v.97, No. 457):77-87.
8. Chen, S. Cowan, CFN. And Grant, PM. (1991). Orthogonal least squares learning algorithm for radial basis functions networks. IEEE Transactions on Neural Networks, 2(2), 302-309.
9. Korenberg, MJ. (1988). Identifying nonlinear difference equation and functional expansion representations: the fast orthogonal algorithm. Ann Biomed, Eng. (16) 123-142.
10. Kendziorski, M, et al. (2003) On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in Medicine 22: 3899-3914.
11. Huberty CJ. (1994) Applied Discriminant Analysis. New York: John Wiley & Sons.
12. Hastie, T, Tibshirani, R, and Friedman, J. (2001).The Elements of Statistical Learning: Data Mining, Inference, and Prediction Springer: New York.
13. Chawla-Sarkar M, et al. (2003) Apoptosis and interferons: Role of interferon-stimulated genes as mediators of apoptosis. Apoptosis 8: 237-249.
14. Gniadek P, et al. (2003) Systemic IFN-beta treatment induces apoptosis of peripheral immune cells in MS patients. J Neuroimmunol 137: 187-196.
15. Xia, JX. et al. (2005). B7 molecules mRNA expression in colorectal carcinoma. World J Gastroenterol.Sep 28;11(36):5655-8.