Plants are fascinating and complex organisms. A comprehensive understanding of the
organization, function and evolution of plant genes is essential to disentangle important …

Applications of clustering algorithms in biomedical research are ubiquitous, with typical
examples including gene expression data analysis, genomic sequence analysis, biomedical …

Biclustering has become a popular technique for the study of gene expression data,
especially for discovering functionally related gene sets under different subsets of …

Cloud computing promises high scalability, flexibility and cost-effectiveness to satisfy
emerging computing requirements. To efficiently provision computing resources in the cloud …

Motivation: Biclustering of transcriptomic data groups genes and samples simultaneously. It
is emerging as a standard tool for extracting knowledge from gene expression …

The need to analyze high-dimension biological data is driving the development of new data
mining methods. Biclustering algorithms have been successfully applied to gene expression …

Background Biclustering techniques are capable of simultaneously clustering rows and
columns of a data matrix. These techniques became very popular for the analysis of gene …

Nonalcoholic fatty liver disease (NAFLD) is a common metabolic dysfunction leading to
hepatic steatosis. However, NAFLD's global impact on the liver lipidome is poorly …

Co-expression network analysis is one of the most powerful approaches for interpretation of
large transcriptomic datasets. It enables characterization of modules of co-expressed genes …

Supplying a comprehensive overview of healthcare analytics research, Healthcare Data
Analytics provides an understanding of the analytical techniques currently available to solve …