The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Clustering short time series gene expression data
Bioinformatics
Performing hypothesis tests on the shape of functional data
Computational Statistics & Data Analysis
Support vector machine for functional data classification
Neurocomputing
Functional classification in Hilbert spaces
IEEE Transactions on Information Theory
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Editorial: Computational statistics within clinical research
Computational Statistics & Data Analysis
Supervised classification using probabilistic decision graphs
Computational Statistics & Data Analysis
Adaptive clustering for time series: Application for identifying cell cycle expressed genes
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Which Distance for the Identification and the Differentiation of Cell-Cycle Expressed Genes?
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Pattern Recognition Letters
HIV-1 CRF01_AE coreceptor usage prediction using kernel methods based logistic model trees
Computers in Biology and Medicine
A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples
Computational Statistics & Data Analysis
Polarization of forecast densities: A new approach to time series classification
Computational Statistics & Data Analysis
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Since most biological systems are developmental and dynamic, time-course gene expression profiles provide an important characterization of gene functions. Assigning functions for genes with unknown functions based on time-course gene expressions is an important task in functional genomics. Recently, various methods have been proposed for the classification of gene functions based on time-course gene expression data. In this paper, we consider the classification of gene functions from functional data analysis viewpoint, where a functional support vector machine is adopted. The functional support vector machine can model temporal effects of time-course gene expression data by incorporating the coefficients as well as the basis matrix obtained from a finite expansion of gene expressions on a set of basis functions. We apply the functional support vector machine to both real microarray and simulated data. Our results indicate that the functional support vector machine is effective in discriminating gene functions of time-course gene expressions with predefined functions. The method also provides valuable functional information about interactions between genes and allows the assignment of new functions to genes with unknown functions.