Abstract
AbstractThe failure of biology to identify the molecular causes of disease has led to disappointment in the rate of development of new medicines. By combining the power of community-based modeling with broad access to large datasets on a platform that promotes reproducible analyses we can work towards more predictive molecular maps that can deliver better therapeutics.
Highlights
Drugs continue to fail in clinical development at a startlingly high rate despite unprecedented amounts of investment in research and development, largely as a result of a lack of efficacy in phase 2 trials[1]
Biology is rapidly becoming a science that is driven by technology and large-scale data
The challenge of generating predictive molecular models of disease is complex and is not likely to be solved by any one group of researchers. It will be necessary for researchers in the field of biology to adopt the community-based practices that have proven successful in other areas of science and technology
Summary
Extend the work of others will require that the data and methods used be distributed in a manner that is both accessible and usable. We advocate the concept of a ‘Commons’, in which contributor scientists can collaborate in transparent and structured ways to build better maps of disease from a common reference of curated data In this vision, the contributors are not people who upload or download data for isolated use but, instead, they are active participants that build collective content in a manner analogous to other distributed community projects, such as Wikipedia. In this Perspective, we describe key aspects of the Sage Bionetworks Commons project, including the efforts made to date in building a computational platform and a data and model repository that includes the associated analysis tools, as well as the development of data sharing rules and policies We explain how this environment will drive us toward the generation of better maps of disease and become a forum for reproducible and reusable data and analyses. This process will guide biological researchers to high quality analytical results from which they can inform their own research efforts
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