Biclustering algorithms have shown to be remarkably effective in a variety of applications. Although the biclustering problem is known to be NP-complete, in the particular case of time series gene expression data analysis, efficient and complete biclustering algorithms, are known and have been used to identify biologically relevant expression patterns. However, these algorithms, namely CCC-Bicl...

BACKGROUND Biclustering is a powerful technique for identification of co-expressed gene groups under any (unspecified) substantial subset of given experimental conditions, which can be used for elucidation of transcriptionally co-regulated genes. RESULTS We have previously developed a biclustering algorithm, QUBIC, which can solve more general biclustering problems than previous biclustering ...

Most of the biclustering/projected clustering algorithms are based either on the Euclidean distance or correlation coefficient which capture only linear relationships. However, in many applications, like gene expression data and word-document data, non linear relationships may exist between the objects. Mutual Information between two variables provides a more general criterion to investigate de...

The important research objective of identifying genes with similar behavior with respect to different conditions has recently been tackled with biclustering techniques. In this paper we introduce a new approach to the biclustering problem using the Possibilistic Clustering paradigm. The proposed Possibilistic Biclustering algorithm finds one bicluster at a time, assigning a membership to the bi...

Multiple Structure Recovery (MSR) represents an important and challenging problem in the field of Computer Vision and Pattern Recognition. Recent approaches to MSR advocate the use of clustering techniques. In this paper we propose an alternative method which investigates the usage of biclustering in MSR scenario. The main idea behind the use of biclustering approaches to MSR is to isolate subs...

Biclustering has been widely applied in recent years. Various algorithms have developed to perform biclustering various cases. However, only a few studies evaluated the performance of bicluster algorithms. Therefore, this study evaluates algorithms, namely Cheng and Church algorithm (CC algorithm) Iterative Signature Algorithm (ISA). Evaluation is carried out form comparative results terms memb...

In this paper, we present a new algorithm called, BiBinConvmean, for biclustering of binary microarray data. It is a novel alternative to extract biclusters from sparse binary datasets. Our algorithm is based on Iterative Row and Column Clustering Combination (IRCCC) and Divide and Conquer (DC) approaches, K-means initialization and the CroBin evaluation function [6]. Applied on binary syntheti...

In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced. The method called EBIC aims to detect biologically meaningful, order-preserving patterns in complex data. The proposed algorithm is probably the first one capable of discovering with accuracy exceeding 50% multiple complex patterns in real gene expression datasets. It is also one of the very few bi...

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 prior knowledge and expertise from the user. To this end, query-based biclustering algorithms that are rece...

Biclustering is the unsupervised learning task of mining a data matrix for useful submatrices, for instance groups of genes that are co-expressed under particular biological conditions. As these submatrices are expected to partly overlap, a significant challenge in biclustering is to develop methods that are able to detect overlapping biclusters. The authors propose a probabilistic mixture mode...