A General Framework for Analyzing Data from Two Short Time-Series Microarray Experiments
PrePrint
ISSN: 1545-5963
DOI Bookmark:
http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.51
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 datasets, are found to be both biologically meaningful and consistent with published studies.
Index Terms:
Machine learning, Learning, Artificial Intelligence, Computing Methodologies, Bioinformatics (genome or protein) databases, Clustering, classification, and association rules, Information, Feature extraction or construction, Mining methods and algorithms, Database Applications, Database Management, Information Technology and Systems
Citation:
Mohak Shah, Jacques Corbeil, "A General Framework for Analyzing Data from Two Short Time-Series Microarray Experiments," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11 May. 2009. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.51>
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