A Nanopublication Framework for Systems Biology and Drug Repurposing

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Abstract:

Systems biology studies interactions between proteins, genes, drugs, and other molecular entities. A number of databases have been developed that serve as a patchwork across the landscape of systems biology, focusing on different experimental methods, many species, and a wide diversity of inclusion criteria. Systems biology has been used in the past to generate hypotheses for drug effects, but has become fragmented under the large number of disparate and disconnected databases. In our efforts to create a systematic approach to discovering new uses for existing drugs, we have developed Repurposing Drugs with Semantics (ReDrugS). Our ReDrugS framework can accept data from nearly any database that contains biological or chemical entity interactions. We represent this information as sets of nanopublications, fine-grained assertions that are tied to descriptions of their attribution and supporting provenance. These nanopublications are required to have descriptions of the experimental methods used to justify their assertions. By inferring the probability of truth from those experimental methods, we are able to create consensus assertions, along with a combined probability. Those consensus assertions can be searched for via a set of Semantic Automated Discovery and Integration (SADI) web services, which are used to drive a demonstration web interface. We then show how associations between exemplar drugs and cancer-driving genes can be explored and discovered. Future work will incorporate protein/disease associations, perform hypothesis generation on indirect drug targets, and test the resulting hypotheses using high throughput drug screening.

History

DateCreated ByLink
March 7, 2014
10:47:57
James McCuskerDownload
March 5, 2014
15:20:31
James McCuskerDownload
March 5, 2014
15:15:53
James McCuskerDownload

Related Projects:

Repurposing Drugs with Semantics (ReDrugS)
Principal Investigator: Jonathan Dordick and Deborah L. McGuinness
Description: We aim to find new effective treatments for disease using existing drugs. Our approach is to gather and integrate existing data using semantic technologies to help discover promising drug repurposing.

Related Research Areas:

Data Frameworks
Lead Professor: Peter Fox
Description: None.
Concepts:
Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.

Concepts:
Inference And Trust
Lead Professor: Deborah L. McGuinness
Description: Inference And Trust
Concepts:
Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance,
Semantic eScience
Lead Professor: Peter Fox
Description:
Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.
As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.
Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts: