Kernel independent component analy- sis, 2001. ,
A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes, Bioinformatics, vol.17, issue.6, pp.509-519, 2001. ,
DOI : 10.1093/bioinformatics/17.6.509
A clustering technique for summarizing multivariate data, Behavioral Science, vol.27, issue.2, pp.153-155, 1967. ,
DOI : 10.1002/bs.3830120210
Tissue Classification with Gene Expression Profiles, Journal of Computational Biology, vol.7, issue.3-4, pp.559-584, 2000. ,
DOI : 10.1089/106652700750050943
Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society B, vol.57, pp.289-300, 1995. ,
Selection of relevant features and examples in machine learning, Artificial Intelligence, vol.97, issue.1-2, pp.245-271, 1997. ,
DOI : 10.1016/S0004-3702(97)00063-5
Classification and regression trees, 1984. ,
A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.121-167, 1998. ,
DOI : 10.1023/A:1009715923555
The use of maximum likelihood estimates in chi2 tests for goodness-of-fit, The Annals of Mathematical Statistics, vol.25, pp.576-586, 1954. ,
A Review of Classification, Journal of the Royal Statistical Society. Series A (General), vol.134, issue.3, pp.321-367, 1971. ,
DOI : 10.2307/2344237
Multidimensional Scaling, 1994. ,
DOI : 10.1007/978-3-540-33037-0_14
Maximum likelihood from incomplete data via the em algorithm, J. of the Royal Statistical Society B, vol.34, pp.1-38, 1977. ,
Implementing partial least squares, Statistics and Computing, vol.52, issue.2, 1994. ,
DOI : 10.1007/BF00142661
Some comments on maximum likelihood and partial least squares methods, Journal of Econometrics, vol.22, issue.1-2, pp.67-90, 1983. ,
DOI : 10.1016/0304-4076(83)90094-5
Pattern Classification, 2000. ,
Cluster analysis and display of genome-wide expression patterns, Proceedings of the National Academy of Sciences, vol.95, issue.25, pp.9514863-14868, 1998. ,
DOI : 10.1073/pnas.95.25.14863
Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, vol.16, issue.10, pp.16906-914, 2000. ,
DOI : 10.1093/bioinformatics/16.10.906
Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring, Science, vol.286, issue.5439, pp.531-537, 1999. ,
DOI : 10.1126/science.286.5439.531
Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses, 1994. ,
The probable error of a mean, BIOMETRIKA, vol.6, pp.1-25, 1908. ,
An introduction to variable and feature selection, Journal of Machine Learning Research, 2003. ,
The meaning and use of the area under a receiver operating characteristic (ROC) curve., Radiology, vol.143, issue.1, pp.29-36, 1982. ,
DOI : 10.1148/radiology.143.1.7063747
The Elements of Statistical Learning, 2001. ,
Principal Component Analysis, 1986. ,
DOI : 10.1007/978-1-4757-1904-8
Blind separation of sources, part 1: An adaptive algorithm based on neuromimetic architecture. Signal Process, pp.1-10, 1991. ,
THE TREATMENT OF TIES IN RANKING PROBLEMS, Biometrika, vol.33, issue.3, pp.239-251, 1945. ,
DOI : 10.1093/biomet/33.3.239
The Wrapper Approach, 1998. ,
DOI : 10.1007/978-1-4615-5725-8_3
Survey of genetic algorithms and genetic programming, Proceedings of WESCON'95, 1995. ,
DOI : 10.1109/WESCON.1995.485447
Computer Assisted Multicrossvalidation in Regression Analysis, Educational and Psychological Measurement, vol.42, issue.1, pp.187-193, 1982. ,
DOI : 10.1177/0013164482421019
Chi2: Feature selection and discretization of numeric attributes, 1995. ,
Bayesian Methods for Adaptive Models, 1992. ,
Clustering via normal mixture models, 1997. ,
Some methods for classification and analysis of multivariate observations, Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp.281-97, 1967. ,
Assessing relevance determination methods using delve generalization in neural networks and machine learning, 1998. ,
caGEDA, Applied Bioinformatics, vol.3, issue.1, pp.49-62, 2004. ,
DOI : 10.2165/00822942-200403010-00007
Gene functional classification from heterogeneous data, Proceedings of the fifth annual international conference on Computational biology , RECOMB '01, pp.249-255, 2001. ,
DOI : 10.1145/369133.369228
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.9529
Systematic variation in gene expression patterns in human cancer cell
lines, Nature Genetics, vol.59, issue.3, pp.227-235, 2000. ,
DOI : 10.1126/science.278.5342.1481
Artificial Intelligence, 1995. ,
Learning with Kernels Class prediction and discovery using gene expression data, 2000. ,
An information theoretic approach to rule induction from databases, IEEE Transactions on Knowledge and Data Engineering, vol.4, issue.4, pp.301-316, 1992. ,
DOI : 10.1109/69.149926
Spectral Clustering Gene Ontology Terms to Group Genes by Function, 2005. ,
DOI : 10.1007/11557067_1
The analysis of gene expression data: methods and software, chapter SAM thresholding and false discovery rates for detecting differential gene expression in DNA microarrays, 2003. ,
Significance analysis of microarrays applied to the ionizing radiation response, Proceedings of the National Academy of Sciences, vol.98, issue.9, pp.5116-5121, 2001. ,
DOI : 10.1073/pnas.091062498
The mutual information principle and applications, Information and Control, vol.22, issue.1, pp.1-12, 1973. ,
DOI : 10.1016/S0019-9958(73)90448-8
The nature of statistical learning theory, 1995. ,
Resamplingbased multiple testing: examples and methods for p-value adjustment, 1993. ,
Individual Comparisons by Ranking Methods, Biometrics Bulletin, vol.1, issue.6, pp.80-83, 1945. ,
DOI : 10.2307/3001968
Feature selection for high-dimensional genomic microarray data, Proc. 18th International Conf. on Machine Learning, pp.601-608, 2001. ,