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| ASCII Text | x | ||
| Curtis Huttenhower, Olga Troyanskaya, "A General Methodology for Integration of Microarray Data," 2005 IEEE Computational Systems Bioinformatics Conference - Workshops, pp. 109, 2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05), 2005. | |||
| BibTex | x | ||
| @article{ 10.1109/CSBW.2005.8, author = {Curtis Huttenhower and Olga Troyanskaya}, title = {A General Methodology for Integration of Microarray Data}, journal ={2005 IEEE Computational Systems Bioinformatics Conference - Workshops}, volume = {0}, year = {2005}, isbn = {0-7695-2442-7}, pages = {109}, doi = {http://doi.ieeecomputersociety.org/10.1109/CSBW.2005.8}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2005 IEEE Computational Systems Bioinformatics Conference - Workshops TI - A General Methodology for Integration of Microarray Data SN - 0-7695-2442-7 SP EP A1 - Curtis Huttenhower, A1 - Olga Troyanskaya, PY - 2005 KW - null VL - 0 JA - 2005 IEEE Computational Systems Bioinformatics Conference - Workshops ER - | |||
Microarray datasets tend to explore specific areas of biological function; integration of multiple microarrays allows construction of a more complete picture, but it can be difficult to combine independent datasets. Previous methods have attempted to integrate microarray data either purely statistically (Choi, 2003; Detours, 2003; Moreau, 2003) or for specific tasks (Ng, 2003; Pavlidis, 2003; Imoto, 2002; Hartemink, 2001). However, no general method for integration of microarray data with a focus on biological function has yet been proposed.
We present a method for the integration of microarray datasets employing a fixed structure Bayesian network. Rather than learning all interactions simultaneously, we focus on undirected functional interactions between pairs of genes. Using Expectation Maximization, we learn one set of network parameters per functional category of interest.