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IEEE/ACM Trans Comput Biol Bioinform. 2011 Jan-Mar;8(1):14-26. doi: 10.1109/TCBB.2009.51.

A general framework for analyzing data from two short time-series microarray experiments.

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Centre for Intelligent Machines, McGill University, McConnell Engineering Building, Room 444, 3480, University Street, Montreal, QC H3A 2A7, Canada.


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.

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