Learn More
The biclustering problem of gene expression data deals with finding a subset of genes which exhibit similar expression patterns along a subset of conditions. Most of the current algorithms use a statistically predefined threshold as an input parameter for biclustering. This threshold defines the maximum allowable dissimilarity between the cells of a(More)
Biclustering algorithms perform simultaneous row and column clustering of a given data matrix. In gene expression dataset a bicluster is a subset of genes that exhibit similar expression patterns through a subset of conditions. Biclustering is a useful data mining technique for identifying local patterns from gene expression data. In this paper biclusters(More)
— This paper extends a method for integrating source-code model checking with dynamic system analysis to verify properties of controllers for nonlinear dynamic systems. Source-code model checking verifies the correctness of control systems including features that are introduced by the software implementation, such as concurrency and task interleaving. Sets(More)
— Model checkers for program verification have enjoyed considerable success in recent years. In the control systems domain, however, they suffer from an inability to account for the physical environment. For control systems, simulation is the most widely used approach for validating system designs. We present a new technique that uses a software model(More)
Microarray technology demands the development of data mining algorithms for extracting useful and novel patterns. A bicluster of a gene expression dataset is a local pattern such that the genes in the bicluster exhibit similar expression patterns through a subset of conditions. In this study biclusters are detected in two steps. In the first step high(More)
  • 1