an important step in considering of gene expression data is obtained groups of genes that have similarity patterns. biclustering methods was recently introduced for discovering subsets of genes that have coherent values across a subset of conditions. the las algorithm relies on a heuristic randomized search to find biclusters. in this paper, we introduce biclustering las algorithm and then appl...

Cheng-Church (CC) biclustering algorithm is the popular algorithm for the gene expression data mining at present. Only find one biclustering can be found at one time and the biclustering that overlap each other can hardly be found when using this algorithm. This article puts forward a modified algorithm for the gene expression data mining that uses the middle biclustering result to conduct the ...

Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the genera...

The availability of large microarray data has brought along many challenges for biological data mining. Following Cheng and Church [4], many different biclustering methods have been widely used to find appropriate subsets of experimental conditions. Still no paper directly optimizes or bounds the Mean Squared Residue (MSR) originally suggested by Cheng and Church. Their algorithm, for a given e...

Biclustering is an important tool in microarray analysis when only a subset of genes co-regulates in a subset of conditions. Different from standard clustering analyses, biclustering performs simultaneous classification in both gene and condition directions in a microarray data matrix. However, the biclustering problem is inherently intractable and computationally complex. In this paper, we pre...

In the context of microarray data analysis, biclustering aims to identify simultaneously a group of genes that are highly correlated across a group of experimental conditions. This paper presents a Biclustering Iterative Local Search (BILS) algorithm to the problem of biclustering of microarray data. The proposed algorithm is highlighted by the use of some original features including a new eval...

Biclustering refers to simultaneous clustering of objects and their features. Use of biclustering is gaining momentum in areas such as text mining, gene expression analysis and collaborative filtering. Due to requirements for high performance in large scale data processing applications such as Collaborative filtering in E-commerce systems and large scale genome-wide gene expression analysis in ...

Biclustering methods have proven to be critical tools in the exploratory analysis of high-dimensional data including information networks, microarray experiments, and bag of words data. However, most biclustering methods fail to answer specific questions of interest and do not incorporate background knowledge and expertise from the user. To this end, query-based biclustering algorithms have bee...

Biclustering has been suggested and found very useful to discover gene regulation patterns from gene expression microarrays. Several quantitative algorithms, among others CC and BIMAX, have been implemented in R, mainly by the biclust package. To our best knowledge, there have been so far no qualitative biclustering methods implemented. Therefore we introduce rqubic, a Bioconductor package impl...

In the course we have already seen different Biclustering methods such as Cheng-Church, ISA, SAMBA (see scribe 5), OPSM (see scribe 9). The method described in this lecture is Bimax an algorithm due to Prelić et al. [2]. It uses a simple data model reflecting the fundamental idea of biclustering, while aiming to determine all optimal biclusters in reasonable time. This method has the benefit of...