![]() ![]() Dual oxidase 2 is essential for the toll-like receptor 5-mediated inflammatory response in airway mucosa. Joo JH, Ryu JH, Kim CH, Kim HJ, Suh MS, Kim JO, Chung SY, Lee SN, Kim HM, Bae YS, et al. VisANT: an online visualization and analysis tool for biological interaction data. Agile software development: The business of innovation. Web.ĭeFronzo RA, Ferrannini E, Groop L, Henry RR, Herman WH, Holst JJ, Hu FB, Kahn CR, Raz I, Shulman GI, et al. Database: The Journal of Biological Databases and Curation. A CTD-Pfizer collaboration: manual curation of 88000 scientific articles text mined for drug-disease and drug-phenotype interactions. ĭavis AP, Wiegers TC, Roberts PM, King BL, Lay JM, Lennon-Hopkins K, Sciaky D, Johnson R, Keating H, Greene N, et al. The GeneDive application and information about its underlying system architecture are available at. In the near future, GeneDive will seamlessly accommodate other interaction types, such as gene-drug and gene-disease interactions, thus enabling full exploration of topics such as precision medicine. solani possess either homothallic (self-fertile) or bipolar heterothallic mating systems. For over half of the curated gene sets sourced from four prominent databases, more than 80% of the gene set members are recovered by GeneDive. Through the production of recombinant genotypes, sexual populations maintain higher genotype diversity than asexual populations that may have the same gene diversity(Ciampi et al.,2008). GeneDive currently processes over three million gene-gene interactions with response times within a few seconds. GeneDive offers various features and modalities that guide the user through the search process to efficiently reach the information of their interest. To this end we have developed GeneDive, a web-based information retrieval, filtering, and visualization tool for large volumes of gene interaction data. ![]() A tool for efficient querying and visualization of biomedical data that helps researchers understand the underlying biological mechanisms for diseases and drug responses, and ultimately helps patients, is sorely needed. With the rise in text mining approaches, the volume of such biomedical data is rapidly increasing, thereby creating a new problem for the users of this data: information overload. Obtaining relevant information about gene interactions is critical for understanding disease processes and treatment. ![]()
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