Epstein-Barr Virus (EBV) is the first human virus related to oncogenesis.EBV infection is associated with inflammatory bowel disease (IBD) with unknown causality.The prevalence of EBV in intestinal tissue from patients with IBD is significantly higher and related to the exacerbation of the disease and refractory IBD.Immunosuppressive therapy has improved outcomes associated with IBD.However,it is also associated with an increased risk of opportunistic infection,and lymphoproliferative disorders (LDs) maybe due to EBV infection.Here we review our current understanding of the pathogenesis of EBV infection in colonic mucosal inflammation,EBV-induced disease exacerbation,lymphomagenesis in IBD,and clinical approaches therefrom.

Multidimensional genome-wide data (e.g., gene expression microarray data) provide rich information and widespread applications in integrative biology. However, little attention has been paid to the inherent relationships within these natural data. By simply viewing multidimensional microarray data scattered over hyperspace, the spatial properties (topological structure) of the data clouds may reveal the underlying relationships. Based on this idea, we herein make analytical improvements by introducing a topology-preserving selection and clustering (TPSC) approach to complex large-scale microarray data. Specifically, the integration of self-organizing map (SOM) and singular value decomposition allows genome-wide selection on sound foundations of statistical inference. Moreover, this approach is complemented with an SOM-based two-phase gene clustering procedure, allowing the topology-preserving identification of gene clusters. These gene clusters with highly similar expression patterns can facilitate many aspects of biological interpretations in terms of functional and regulatory relevance. As demonstrated by processing large and complex datasets of the human cell cycle, stress responses, and host cell responses to pathogen infection, our proposed method can yield better characteristic features from the whole datasets compared to conventional routines. We hence conclude that the topology-preserving selection and clustering without a priori assumption on data structure allow the in-depth mining of biological information in a more accurate and unbiased manner. A Web server (http://www.cs.bris.ac.uk/∼hfang/TPSC) hosting a MATLAB package that implements the methodology is freely available to both academic and nonacademic users. These advances will expand the scope of omics applications.