Recent Patents on Biclustering Algorithms for Gene Expression Data Analysis
Abstract:In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits a consistent pattern over a subset of conditions. Although used extensively in gene expression data analysis, conventional clustering algorithms that consider the entire row or column in an expression matrix can therefore fail to detect useful patterns in the data. Recently, biclustering has been proposed as a powerful computational tool to detect subsets of genes that exhibit consistent pattern over subsets of conditions. In this article, we review several recent patents in bicluster analysis, and in particular, highlight a recent patent from our group about a novel geometric-based biclustering method that handles the class of bicluster patterns with linear coherent variation across the row and/or column dimension. This class of bicluster patterns is of particular importance since it subsumes all constant, additive, and multiplicative bicluster patterns normally used in gene expression data analysis.
Keywords: Biclustering; Gaussian mixtures; Statistical-Algorithmic Method for Bicluster Analysis; biclustering algorithms; cluster analysis; data matrix; gene expression data; geometric-based biclustering; hierarchical clustering; localized groupings; microarray data; multidimensional data analysis; multiplicative coherent values; pattern discovery; significant homogeneity
Document Type: Research Article
Publication date: August 1, 2011
- Recent Patents on DNA and Gene Sequences publishes review articles by experts on recent patents on DNA and gene sequences. A selection of important and recent patents in the field is also included in the journal. The journal is essential reading for all researchers involved in applied molecular biology.