Paper Info
Title
A General Methodology for Integration of Microarray Data
Abstract
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.
Year
DOI
Venue
2005
10.1109/CSBW.2005.8
CSB Workshops
Keywords
Field
DocType
general methodology,specific area,biological function,network parameter,microarray datasets,functional category,fixed structure bayesian network,previous method,microarray data,general method,independent datasets,expectation maximization,bayesian network,genetics,data integrity
Data mining,Microarray,Expectation–maximization algorithm,Computer science,Data integrity,Bayesian network,Microarray analysis techniques,Artificial intelligence,Bioinformatics,DNA microarray,Machine learning,Network structure
Conference
ISBN
Citations 
PageRank 
0-7695-2442-7
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Curtis Huttenhower143830.18
O. G. Troyanskaya21733144.94