Background Co-expression measures are often used to define networks among genes.
Mutual information (MI) is often used as a generalized correlation measure. It is not clear …

Gene co-expression networks capture biological relationships between genes and are
important tools in predicting gene function and understanding disease mechanisms. We …

Identification of differentially expressed pathways from expression data is an important
problem because it allows us to gain insights into the functional working mechanism of cells …

Motivation: Microarray-based gene expression studies have great potential but are
frequently difficult to interpret due to their overwhelming dimensions. Recent studies have …

The field of gene expression data analysis has grown in the past few years from being
purely data-centric to integrative, aiming at complementing microarray analysis with data …

Motivation: RNA-Seq experiments have shown great potential for transcriptome profiling.
While sequencing increases the level of biological detail, integrative data analysis is also …

Understanding diseases requires identifying the differences between healthy and affected
tissues. Gene expression data have revolutionized the study of diseases by making it …

▪ Abstract The availability of complete genome sequences and the wealth of large-scale
biological data sets now provide an unprecedented opportunity to elucidate the genetic …

Genes interact in networks to orchestrate cellular processes. Analysis of these networks
provides insights into gene interactions and functions. Here, we took advantage of normal …

High-throughput measurements of gene expression and genetic marker data facilitate
systems biologic and systems genetic data analysis strategies. Gene co-expression …