The nature of statistical learning theory
The nature of statistical learning theory
A tutorial on support vector regression
Statistics and Computing
Analyzing time series gene expression data
Bioinformatics
Clustering of time-course gene expression data using functional data analysis
Computational Biology and Chemistry
Computational Biology and Chemistry
On a new class of framelet kernels for support vector regression and regularization networks
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Framelet kernels with applications to support vector regression and regularization networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Gene time series microarray experiments have been widely used to unravel the genetic machinery of biological process However, most temporal gene expression data often contain noise, missing data points, and non-uniformly sampled time points, which will make the traditional analyzing methods to be unapplicable One main approach to solve this problem is to reconstruct each gene expression profile as a continuous function of time Then the continuous representation enables us to overcome problems related to sampling rate differences and missing values In this paper, we introduce a novel reconstruction approach based on the support vector regression method The proposed approach utilizes a framelet based kernel, which has the ability to approximate functions with multiscale structure and can reduce the influence of noise in data To compensate the inadequate information from noisy and short gene expression data, we use its correlated genes as the test set to choose the optimal parameters We show that this treatment can help to avoid over-fitting Experimental results demonstrate that our method can improve the reconstruction accuracy.