Paper Info
Title
Using formal concept analysis for microarray data comparison.
Abstract
Microarray technologies, which can measure tens of thousands of gene expression values simultaneously in a single experiment, have become a common research method for biomedical researchers. Computational tools to analyze microarray data for biological discovery are needed. In this paper, we investigate the feasibility of using Formal Concept Analysis (FCA) as a tool for microarray data analysis. The method of FCA builds a (concept) lattice from the experimental data together with additional biological information. For microarray data, each vertex of the lattice corresponds to a subset of genes that are grouped together according to their expression values and some biological information related to gene function. The lattice structure of these gene sets might reflect biological relationships in the dataset. Similarities and differences between experiments can then be investigated by comparing their corresponding lattices according to various graph measures. We apply our method to microarray data derived from influenza infected mouse lung tissue and healthy controls. Our preliminary results show the promise of our method as a tool for microarray data analysis.
Year
DOI
Venue
2007
10.1142/9781860947995_0009
Series on Advances in Bioinformatics and Computational Biology
Keywords
Field
DocType
formal concept analysis,gene expression,microarray data,microarray data analysis
Research method,Graph,Data mining,Microarray,Experimental data,Microarray analysis techniques,Bioinformatics,Gene chip analysis,Microarray databases,Formal concept analysis,Mathematics
Conference
Volume
Issue
ISSN
5
1
1751-6404
Citations 
PageRank 
References 
10
0.68
7
Authors
7
Name
Order
Citations
PageRank
vicky choi1100.68
yalou huang2100.68
Lam, V.3884.77
Lam, V.4884.77
david h potter5100.68
Reinhard C. Laubenbacher69111.98
karen duca7100.68