In recent years, with the development of microarray technique, discovery of useful knowledge from microarray data has become very important. Biclustering is a very useful data mining technique for discovering genes which have similar behavior. In microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A Multi-Objective ...

Privacy concerns are a major hurdle in the success of personalized services in ubiquitous computing environments. Personalized recommendations are usually served using Collaborative Filtering techniques. In this paper, we propose a framework for privacy preserving collaborative filtering in ubiquitous computing environments. The proposed framework is based on a biclustering algorithm which empl...

Consider a dataset where data is collected on multiple features of multiple individuals over multiple times. This type of data can be represented as a three dimensional individual/feature/time tensor and has become increasingly prominent in various areas of science. The tensor biclustering problem computes a subset of individuals and a subset of features whose signal trajectories over time lie ...

Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-re...

Biclustering is an increasingly popular technique to identify gene regulatory modules that are linked to biological processes. We describe a novel method, called ProBic, that was developed within the framework of Probabilistic Relational Models (PRMs). ProBic is an efficient biclustering algorithm that simultaneously identifies a set of potentially overlapping biclusters in a gene expression da...

A large number of biclustering methods have been proposed to detect patterns in gene expression data. All these methods try to find some type of biclusters but no one can discover all the types of patterns in the data. Furthermore, researchers have to design new algorithms in order to find new types of biclusters/patterns that interest biologists. In this paper, we propose a novel approach for ...

Biclustering represents an intrinsically complex problem, where the aim is to perform a simultaneous rowand column-clustering of a given data matrix. Some recent approaches model this problem using factor graphs, so to exploit their ability to open the door to efficient optimization approaches for well designed function decompositions. However, while such models provide promising results, they ...

Abstract. We propose a two-step biclustering approach to mine co-regulation patterns of a given reference gene to discover other genes that function in a common biological process. Currently, several successful methods utilize Pearson Correlation Coefficient (PCC) based gene expression analysis across all samples in datasets. However, microarray datasets are fraught with spurious samples or sam...

Biclustering algorithms, which aim to provide an effective and efficient way to analyze gene expression data by finding a group of genes with trend-preserving expression patterns under certain conditions, have been widely developed since Morgan et al. pioneered a work about partitioning a data matrix into submatrices with approximately constant values. However, the identification of general tre...

BACKGROUND A major challenges in the analysis of large and complex biomedical data is to develop an approach for 1) identifying distinct subgroups in the sampled populations, 2) characterizing their relationships among subgroups, and 3) developing a prediction model to classify subgroup memberships of new samples by finding a set of predictors. Each subgroup can represent different pathogen ser...