To compare spatial patterns of gene expression, one must analyze a large number of images as current methods are only able to measure a small number of genes at a time. Bringing i...
Parvez Ahammad, Cyrus L. Harmon, Ann Hammonds, Sha...
We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved timepoints, clustering, and dataset alignment. Each expression p...
Ziv Bar-Joseph, Georg Gerber, David K. Gifford, To...
Analyzing gene expression patterns is becoming a highly relevant task in the Bioinformatics area. This analysis makes it possible to determine the behavior patterns of genes under...
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...
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...