Arcadia Data continues to deliver on its vision of on-cluster analytics by eliminating the complexities of redundant, overlapping security management inherent in BI stacks which rely on moving data off-cluster.
Text mining enables the rapid review and analysis of large volumes of biomedical literature, giving life science companies valuable insights to drive research and development and inform business decisions. For example, the results of mining projects can provide a greater understanding of the underlying biology behind specific diseases and how they respond to certain drugs, and support the target discovery process.
In biomedical research and development, researchers use text mining tools to extract and interpret facts, assertions, and relationships from vast amounts of published information. Mining accelerates the research process, increases discovery of novel findings, and helps companies identify potential safety issues in the drug development process. However, despite the many benefits of text mining, researchers face a number of obstacles before they even get a chance to run queries against the body of biomedical literature.