Classification of gene functions using support vector machine for time-course gene expression data
Computational Statistics & Data Analysis
Journal of Signal Processing Systems
Classification of functional data: A segmentation approach
Computational Statistics & Data Analysis
Cancer classification by gradient LDA technique using microarray gene expression data
Data & Knowledge Engineering
Rough Sets in Oligonucleotide Microarray Data Analysis
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
APPLYING DATA MINING TECHNIQUES FOR CANCER CLASSIFICATION ON GENE EXPRESSION DATA
Cybernetics and Systems
Computational Biology and Chemistry
Variational Bayesian functional PCA
Computational Statistics & Data Analysis
Identifying Non-random Patterns from Gene Expression Profiles
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Temporal gene expression profiles reconstruction by support vector regression and framelet kernel
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Wavelet-RKHS-based functional statistical classification
Advances in Data Analysis and Classification
Computational Statistics & Data Analysis
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Motivation: Temporal gene expression profiles provide an important characterization of gene function, as biological systems are predominantly developmental and dynamic. We propose a method of classifying collections of temporal gene expression curves in which individual expression profiles are modeled as independent realizations of a stochastic process. The method uses a recently developed functional logistic regression tool based on functional principal components, aimed at classifying gene expression curves into known gene groups. The number of eigenfunctions in the classifier can be chosen by leave-one-out cross-validation with the aim of minimizing the classification error. Results: We demonstrate that this methodology provides low-error-rate classification for both yeast cell-cycle gene expression profiles and Dictyostelium cell-type specific gene expression patterns. It also works well in simulations. We compare our functional principal components approach with a B-spline implementation of functional discriminant analysis for the yeast cell-cycle data and simulations. This indicates comparative advantages of our approach which uses fewer eigenfunctions/base functions. The proposed methodology is promising for the analysis of temporal gene expression data and beyond. Availability: MATLAB programs are available upon request. Contact: ileng@wfubmc.edu Supplementary information: Supplementary materials are available on the journal's website.