About the Lung Disease Knowledge Portal project

The Lung Disease Knowledge Portal (LDKP; RRID:SCR_021352) is a database that links DNA sequence, functional and epigenomic information, and clinical data from studies on lung disease and provides analytic tools to analyze these data. The results and analytical tools are accessible to academic and industry researchers, and all 
interested users, to identify and validate changes in DNA that influence the risk of lung disease.

The Knowledge Portal framework is being developed as part of the Accelerating Medicines Partnership, a public-private partnership between the National Institutes of Health (NIH), the U.S. Food and Drug Administration (FDA), 10 biopharmaceutical companies, and multiple non-profit organizations that is managed through the Foundation for the NIH (FNIH). AMP seeks to harness collective capabilities, scale, and resources toward improving current efforts to develop new therapies for complex, heterogeneous diseases. The ultimate goal is to increase the number of new diagnostics and therapies for patients while reducing the time and cost of developing them, by jointly identifying and validating promising biological targets for several diseases, including type 2 diabetes.
The Lung Disease Knowledge Portal is intended to serve three key functions:
  1. To be a central repository for large datasets of human genetic information linked to lung disease and related traits.
  2. To function as a scientific discovery engine that can be harnessed by the community at large, and assist in the selection of new targets for drug design.
  3. Eventually, to facilitate the conduct of customized analyses by any interested user around the world, doing so in a secure manner that provides high quality results while protecting the integrity of the data.
The Lung Disease Knowledge Portal is intended to be secure, compliant with pertinent ethical regulations, accessible to a wide user base, inviting to researchers who may want to contribute data and participate in analyses, organic in the continuous incorporation of scientific advances, modular in its analytical capabilities and user interfaces, automated, rigorous in the quality of data aggregation and returned results, versatile, and sustainable.