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A General Framework for Analyzing Data from Two Short Time-Series Microarray Experiments

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2 Author(s)
Mohak Shah ; McGill University, Montreal ; Jacques Corbeil

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.

Published in:

IEEE/ACM Transactions on Computational Biology and Bioinformatics  (Volume:8 ,  Issue: 1 )