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Bioinformatics insights into acute lung injury/acute respiratory distress syndrome

DOI: 10.1186/2001-1326-1-9

Keywords: Acute lung injury, Acute respiratory distress syndrome, Genomics, Proteomics, Metabolomics, Bioinformatics

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In recent years, there is a great increase in genomics and other molecular research which produced a tremendous amount of information related to molecular biology [1-3]. Meantime, various powerful data mining and statistical bioinformatics methods have been propagated for identifying, prioritizing and classifying robust and generalizable biomarkers with high discriminatory ability [4], and some online bioinformatics data libraries, such as Enzyme, KEGG, Gene Ontology, NCBI Taxonomy, SwissProt and TrEMBL were generated for the store and management of data. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques and theory to solve formal and practical problems arising from the management and analysis of biological data.Clinical bioinformatics is a new emerging science combining clinical information, (including patients’ complaints, history, clinical symptoms and signs, physicians’ examinations, biochemical analysis, imaging profiles, pathologies), omics science, mathematics, information technology and library data together. It may play a potentially important role in the discovery of biomarkers, which will facilitate the diagnosis of disease, as well as treatment. A commonly used definition of a biomarker is “a characteristic that is objectively measured and evaluated as an indicator of normal biological process, pathogenic processes, or pharmacologic responses to a therapeutic intervention” [5]. Clinical bioinformatics is also a new way to focus on the combination of clinical symptoms and signs with human tissue-generated bioinformatics to get deep and full understand of the risk factors, pathogenesis and progress of human disease. Recent technological developments in genomics, proteomics, metabolomics, are allowing researchers to take an unbiased ‘big-picture’ approach to further elucidate the molecular pathogenesis of lung injury [6] (Figure 1). Recent years, clinical bioinformatics have been widel


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