Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
Finding predictive gene groups from microarray data
Journal of Multivariate Analysis
Analyzing tumor gene expression profiles
Artificial Intelligence in Medicine
Computational Statistics & Data Analysis
Microarray data analysis with PCA in a DBMS
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics
Fingerprint Matching Based on Neighboring Information and Penalized Logistic Regression
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
EDA-Based Logistic Regression Applied to Biomarkers Selection in Breast Cancer
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Brief communication: Reducing multiclass cancer classification to binary by output coding and SVM
Computational Biology and Chemistry
Expert Systems with Applications: An International Journal
Multi-platform gene-expression mining and marker gene analysis
International Journal of Data Mining and Bioinformatics
Dimensionality reduction of protein mass spectrometry data using random projection
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Cancer classification by kernel principal component self-regression
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.