BACKGROUND: @entity29 ( @entity29 ) consists of two main disease-subtypes, @entity30 ( @entity30 ) and @entity31 ( @entity31 ); these subtypes share overlapping genetic and clinical features. Genome-wide microarray data enable unbiased documentation of alterations in gene expression that may be disease-specific. As @entity74 are believed to be caused by genetic alterations affecting the function of signalling pathways, module-centric optimisation algorithms, whose aim is to identify sub-networks that are dys-regulated in disease, are emerging as promising approaches. RESULTS: In order to account for the topological structure of molecular interaction networks, we developed an optimisation algorithm that integrates databases of known molecular interactions with gene expression data; such integration enables identification of differentially regulated network modules. We verified the performance of our algorithm by testing it on simulated networks; we then applied the same method to study experimental data derived from microarray analysis of @entity30 and @entity31 biopsies and @entity1 interactome databases. This analysis allowed the extraction of dys-regulated subnetworks under different experimental conditions (inflamed and uninflamed tissues in @entity30 and @entity31 ). Optimisation was performed to highlight differentially expressed network modules that may be common or specific to the disease subtype. CONCLUSIONS: We show that the selected subnetworks include genes and pathways of known relevance for @entity29 ; in particular, the solutions found highlight cross-talk among enriched pathways, mainly the JAK/STAT signalling pathway and the EGF receptor signalling pathway. In addition, integration of gene expression with molecular interaction data highlights nodes that, although not being differentially expressed, interact with differentially expressed nodes and are part of pathways that are relevant to @entity29 . The method proposed here may help identifying dys-regulated sub-networks that are common in different diseases and sub-networks whose dys-regulation is specific to a particular disease.

An integrative analysis of gene expression and molecular interaction data to identify dys-regulated sub-networks in XXXX .

@entity1 :: ('9606', 'Species') :: ['human'] @entity31 :: ('MESH:D003093', 'Disease') :: ['ulcerative colitis', 'UC'] @entity29 :: ('MESH:D015212', 'Disease') :: ['Inflammatory bowel disease', 'IBD'] @entity74 :: ('MESH:D030342', 'Disease') :: ['genetic diseases'] @entity30 :: ('MESH:D003424', 'Disease') :: ['CD', "Crohn's disease"]
@entity29 :: (MESH:D015212,Disease) :: ['inflammatory bowel disease']
