August 2007
The Coming Revolution in Scholarly Communications & Cyberinfrastructure
John Wilbanks, Science Commons

Proof of Concept: E-commerce for biological materials

The Biological Materials Transfer Agreement Project (MTA) develops and deploys standard, modular contracts to lower the costs of transferring physical biological materials such as DNA, cell lines, model animals, antibodies and more. Materials represent tacit knowledge – generating a DNA plasmid or an antibody can take months or years, and replicating the work is rarely feasible. Gaining access to those materials is subject to secrecy, competition, lack of resources to manufacture materials, lack of time, legal transaction costs and delays, and more.

There is significant evidence that the transfer of biological materials is subject to significant slowdowns. Campbell 1 and Cohen 2 have each demonstrated that materials are frequently denied. Legal barriers are part of the problem – more so than patents – but the greater problem is frequently the competition, secrecy, and incentive systems involved.

This is why we brought in funders of disease research and institutional hosts of research from the beginning – this is the part of infrastructure that is social engineering, not software. The secrecy and competition do not maximize the likelihood of meaningful discovery coming from limited funding, and thus funders (especially of rare or orphan diseases) have a particular incentive to maximize the easy movement of biological materials to maximize follow-on research.

The MTA project covers transfers among non-profit institutions as well as between non-profit and for-profit institutions. It integrates existing standard agreements into a Web-deployed suite alongside new Science Commons contracts and allows for the emergence of a transaction system along the lines of Amazon or eBay by using the contracts as a tagging and discovery mechanism for materials.

This metadata driven approach is based on the success of the Creative Commons licensing integration into search engines and further allows for the integration of materials licensing directly into the research literature and databases so that scientists can “one-click” inline as they perform typical research. And like Creative Commons licensing, we can leverage the existing Web technologies to track materials propagation and reuse, creating new data points for the impact of scientific research that are more dimensional than simple citation indices, tying specific materials to related peer-reviewed articles and data sets.

The MTA project was launched in collaboration with the Kauffman Foundation, the iBridge Network of university technology transfer offices, and neurodegenerative disease funders. It currently includes more than 5,000 DNA plasmids covered under standard contracts and is available through the Neurocommons project described in the next section.

Proof of concept in knowledge sharing: a semantic web for neuroscience

In collaboration with the W3C Semantic Web Health Care and Life Science interest group, we are integrating information from a variety of standard sources to establish core interoperable content that can be used as a basis for bioinformatics applications. The combined whole is greater than the sum of its parts, since queries can cut across combinations of sources in arbitrary ways.

We are also providing an operational knowledge base that has a standard, open query endpoint accessible by Internet. The knowledge base incorporates information marshaled from more than a dozen databases, ontologies, and literature sources.

Entities discussed in the text, such as proteins and diseases, need to be specifically identified for computational use, as do the entities' relationships to the text and the text's assertions about the entities (for example, a particular asserted relationship between a protein and a disease). Manual annotation by an author, editor, or other "curator" may capture the text's meaning accurately in a formal notation. However, automated natural language processing (including entity extraction and text mining) is likely to be the only practical method for opening up the literature for computational use.

We were only able to process the abstracts of the literature as the vast majority of the scientific literature is locked behind firewalls and under contracts that explicitly prevent using software to automatically index the full text where it is accessible. Although most papers run more than five pages, the abstracts typically were limited to a paragraph.

For tractability, we limited the scope to the organisms of greatest interest to health care and life sciences research: human, mouse, and rat. We are also providing the opportunity for interested parties to “mirror” the knowledgebase and we encourage its wide reuse and distribution.

In combination with the data integration and text processing, we are also offering a set of analytic tools for use on experimental data. The application of prior knowledge to experimental data can lead to fresh insights. For example, a set of genes or proteins derived from high throughput experiments can be statistically scored against sets of related entities derived from the literature. Particular sets that score well may indicate what's going on in the experimental setting.

In order to help illustrate the value of semantic web practices, we are developing statistical applications that exploit information extracted from RDF data sources, including both conversions of structured information (such as Gene Ontology annotations) and relationships extracted from literature. The first tools we hope to roll out are activity center analysis for gene array data and set scoring for profiling of arbitrary gene sets, donated to Science Commons by Millennium Pharmaceuticals.

Taken together, we call these three efforts the Neurocommons – an open source, open access knowledge management platform, with an initial therapeutic focus on the neurosciences. And we hope to use the Neurocommons both as a platform to facilitate knowledge sharing and to secure empirical evidence as to the value of shared knowledge in sciences.

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Reference this article
Wilbanks, J. "Cyberinfrastructure For Knowledge Sharing," CTWatch Quarterly, Volume 3, Number 3, August 2007. http://www.ctwatch.org/quarterly/articles/2007/08/cyberinfrastructure-for-knowledge-sharing/

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