Database Mining: Defining the Pathogenesis of Inflammatory and Immunological Diseases

Abstract

Cardiovascular disease (CVD) is a leading cause of mortality in developed countries (Jan et al., 2010; Yang et al., 2008). Despite a long held understanding and strong characterization of the traditional and non-traditional risk factors for CVD, some mechanisms of CVD onset have only recently been uncovered. As a chronic inflammatory autoimmune disease, atherosclerosis and its progression involve innate and adaptive immune systems. Using new concepts and technologies to improve the current understandings of the molecular pathogenesis of inflammatory and immune responses would lead to the future development of novel therapeutics for these diseases. Biomedical literature and databases, available in electronic forms, contain a vast amount of knowledge resulting from experimental research (Ishii et al., 2007; Palakal et al., 2007). In the past decade, both traditional hypothesis-driven research and discovery-driven “-omics” research, including genomics, transcriptomics (Liang et al., 2005), proteinomics, metabolomics, glycomics, lipidomics, localizomics, protein-DNA interactomics, proteinprotein interactomics, fluxomics, phenomics (Joyce & Palsson, 2006), and antigen-omics (http://www.cancerimmunity.org/links/databases.htm) (Houle et al., 2010; Shimokawa et al., 2010; Weinstein, 1998;2002), has generated a tremendous amount of data and established many experimental data-based searchable databases. These databases include PubMed, nucleotide database, protein database, and other databases generated by the National Institutes of Health (NIH)/National Center for Biotechnology Information (NCBI) (see the NCBI handbook at http://www.ncbi.nlm.nih.gov/books/NBK21101/) and other institutions. This development has not only provided resources, but also raised unprecedented challenges and opportunities for biomedical scientists to develop more systemic and panoramic approaches to analyze the data contained in the databases and generate new hypotheses. The inconsistency between the vast amount of experimental data, various searchable databases, and relatively smaller numbers of database-mining research papers (< 50 papers on database mining in inflammation and immune responses listed in the PubMed) indicate the challenges that experimental biomedical scientists face, which include both technical/methodological difficulties and out-of-date concepts. Traditionally, medical literature search using the Index Medicus was the major approach for biomedical scientists to identify knowledge gaps and preparing new hypotheses. However, this approach has been significantly enhanced by more systemic approaches such as 1)