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...

DNA microarray data always contains missing values. As subsequent analysis such as biclustering can only be applied on complete data, these missing values have to be imputed before any biclusters can be detected. Existing imputation methods exploit coherence among expression values in the microarray data. In view that biclustering attempts to find correlated expression values within the data, w...

An noticeable number of biclustering approaches have been proposed proposed for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions. In this context, recognizing groups of co-expressed or co-regulated genes, that is, genes which follow a similar expression pattern, is one of the main objectives. Due to t...

Biclustering, which can be defined as the simultaneous clustering of rows and columns in a data matrix, has received increasing attention in recent years, particularly in the field of Bioinformatics (e.g. for the analysis of microarray data). This paper proposes a novel biclustering approach, which extends the Affinity Propagation [1] clustering algorithm to the biclustering case. In particular...

Biclustering (also known as submatrix localization) is a problem of high practical relevance in exploratory analysis of high-dimensional data. We develop a framework for performing statistical inference on biclusters found by score-based algorithms. Since the bicluster was selected in a data dependent manner by a biclustering or localization algorithm, this is a form of selective inference. Our...

Binding of transcription factors to the promoter regions of genes can be measured genome-wide to reveal regulatory networks. The measurements are expensive, however, and methods for predicting bindings from earlier data would reduce the cost. At best, genome-wide studies could be targeted based on a few test samples. We model the binding patterns using recent ideas from collaborative filtering ...

Biclustering is a very useful tool for analyzing microarray data. It aims to identify maximal groups of genes which are coherent with maximal groups of conditions. In this paper, we propose a biclustering algorithm, called BiMine+, which is able to detect significant biclusters from gene expression data. The proposed algorithm is based on two original features. First, BiMine+ is based on the us...