Background: The underlying goal of microarray experiments is to identify gene expression patterns across different experimental conditions. Genes that are contained in a particula...
Johanna S. Hardin, Aya Mitani, Leanne Hicks, Brian...
Several microarray technologies that monitor the level of expression of a large number of genes have recently emerged. Given DNA-microarray data for a set of cells characterized b...
The Edinburgh Mouse Atlas aims to capture in-situ gene expression patterns in a common spatial framework. In this study, we construct a grammar to define spatial regions by combina...
This paper proposes a novel clustering analysis algorithm based on principal component analysis (PCA) and self-organizing maps (SOMs) for clustering the gene expression patterns. T...
Building genetic regulatory networks from time series data of gene expression patterns is an important topic in bioinformatics. Probabilistic Boolean networks (PBNs) have been deve...
In the feature selection of cancer classification problems, many existing methods consider genes individually by choosing the top genes which have the most significant signal-to...
Abstract. We analyse data from the Edinburgh Mouse Atlas GeneExpression Database (EMAGE) which is a high quality data source for spatio-temporal gene expression patterns. Using a n...
It is of high biomedical interest to identify gene interactions and networks that are associated with developmental and physiological functions in the mouse embryo. There are now v...
Liangxiu Han, Jano I. van Hemert, Richard A. Baldo...
The spatio-temporal patterning of gene expression in early embryos is an important source of information for understanding the functions of genes involved in development. Most ana...
Hanchuan Peng, Fuhui Long, Michael B. Eisen, Eugen...
Associating speci c gene activity with speci c functional locations in the brain anatomy results in a greater understanding of the role of the gene's products. To perform such...
Musodiq Bello, Tao Ju, Joe D. Warren, James Carson...