Topics in matrix analysis
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Clustering short time series gene expression data
Bioinformatics
Supervised feature selection via dependence estimation
Proceedings of the 24th international conference on Machine learning
Gene selection via the BAHSIC family of algorithms
Bioinformatics
Clustering of unevenly sampled gene expression time-series data
Fuzzy Sets and Systems
Measuring statistical dependence with hilbert-schmidt norms
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Hierarchical Clustering of High- Throughput Expression Data Based on General Dependences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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We propose a general theoretical framework for analyzing differentially expressed genes and behavior patterns from two homogenous short time-course data. The framework generalizes the recently proposed Hilbert-Schmidt Independence Criterion (HSIC)-based framework adapting it to the time-series scenario by utilizing tensor analysis for data transformation. The proposed framework is effective in yielding criteria that can identify both the differentially expressed genes and time-course patterns of interest between two time-series experiments without requiring to explicitly cluster the data. The results, obtained by applying the proposed framework with a linear kernel formulation, on various data sets are found to be both biologically meaningful and consistent with published studies.