Improved Biclustering Algorithm Based on Weighted Mean Square Residual Microarrays are one of the latest breakthroughs in experimental molecular biology, which have already provided huge amount of high dimensional genetic data. Existing biclustering algorithms can hardly discover biclusters with overlapping structures. Consequently, the correct bicluster structures hidden in gene expression data cannot be effectively found. Moreover, the influence of the importance of the different conditions on the bicustering result is not taken into account in the process of adding and deleting conditions. An improved biclustering algorithm based on weighted mean square residual (IBWMSR) is proposed to overcome the above defects. Our algorithm also proposes a new objective function to update weights of each bicluster, which can simultaneously select the conditions set of each bicluster using some rules. The gene sets are firstly partitioned into initial biclusters by using fuzzy partition and the fuzzy partition is controlled by overlapping ratio and the membership of the genes. Then, the weights of the conditions in each bicluster are iteratively updated in the process of minimizing the objective function. Finally, the bicluster set is obtained after adding the genes satisfying the constraints and removing the genes producing inconsistency fluctuation. The experiment shows that the proposed algorithm generates the biclusters with similar expression level of different sizes and restricts the overlapping ratio to a reasonable range and generate larger biclusters with lower mean square residues.

Gclust: A Parallel Clustering Tool for Microbial Genomic Data Abstract   The accelerating growth of the public microbial genomic data imposes substantial burden on the research community that uses such resources. Building databases for nonredundant reference sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algorithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demonstrate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for noncommercial use at  https:github.comniulabgclust . We also introduce a web server for clustering useruploaded genomes at  http:niulab.scgrid.cngclust .