Correlation Viewer (CV)

The Correlation Viewer (CV) targets specific audiences focused on CT design and sub-population optimization, including clinicians and clinical research organizations (CROs), bioinformaticians and IT professionals.

What current need does Correlation Viewer address?

The Correlation Viewer is a tool for visually exploring correlations between medically/biologically relevant entities such as drugs, diseases, pathways, adverse events etc. Instead of supplying a set of keywords as a query and receiving related documents, as is the case with PubMed, Google and other search engines, the CV supports the user in visually exploring correlations starting from an initial concept of interest, for example a disease or a gene. The user can then explore correlations in a stepwise manner moving ”outwards” from that initial concept. When correlations of interest are identified, the user can “drill down” by requesting the underlying bibliography from which she can extract detailed information. The CV type of search promotes the exploration of hypotheses and also discovery since it shows entities (e. g. pathways) that may be related to the initial entity of interest, but that the user did not think/know to include in her original query. It can therefore serve to highlight correlations between seemingly disparate entities and thereby promote hypothesis generation and subsequently discovery.

In terms of its usability, the CV aims to be as user friendly as any well-known search engine application, like Google, Yahoo and Bing.

How does the Correlation Viewer tool work?

The user interface is simple and self-explanatory: a single text field on the center is the main user input entry, and it expects the user to type a drug name by default. The list field on the left can be used to determine the type of queries the text field accepts, which is Drugs, Diseases or Pathways with relevant bibliographic links.

Correlation Viewer Features


  • Comprehensive coverage of Medline
  • Continuous update
  • Biannual update of terminology (ontology of diseases, pathways, drugs etc.) – over 55 million terms monitored
  • Ontologies used internally: MESH, UMLS
  • Results are filtered against FDA Adverse Events Reporting System (FAERS)
  • Covers concept classes: Drug, Pathway, Disease, Adverse Events


  • Runs on modern browsers
  • Available on PCs and Tablets
  • Based on HTML 5 technology

Further information

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