Issue No. 01 - January-February (2011 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.51
Mohak Shah , McGill University, Montreal
Jacques Corbeil , Laval University, Quebec
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.
Short time-series microarray data, HSIC, differentially expressed genes, gene behavior patterns.
J. Corbeil and M. Shah, "A General Framework for Analyzing Data from Two Short Time-Series Microarray Experiments," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 14-26, 2009.