The current, fully integrated business model of large pharmaceutical companies is increasingly considered to be unsustainable, and so new approaches that engage large and small companies, governments and academic institutions are needed. Could 'open innovation' models that have proved successful in other sectors be fruitfully adopted by the pharmaceutical industry?

Semantic web is a set of formats and languages that find and analyze data on the World Wide Web, a system that pinpoints genetic cause of heart disease and another system that reveals early stages of influenza outbreaks. The companies working through World Wide Web Consortium are developing standards that are making Semantic Web more accessible and easy to use.

The integration of disparate biomedical data continues to be a challenge for drug discovery efforts. Semantic Web technologies provide the capability to more easily aggregate data and thus can be utilized to improve the efficiency of drug discovery. We describe an implementation of a Semantic Web infrastructure that utilizes the scalable Oracle Resource Description Framework (RDF) Data Model as the repository and Seamark Navigator for browsing and searching the data. The paper presents a use case that identifies gene biomarkers of interest and uses the Semantic Web infrastructure to annotate the data.

Presently, neuroscientists have access to a wide range of neuroscience databases through the Internet. However, most of these databases are neither integrated nor interoperating, which creates a barrier in answering complex neuroscience research questions. Agreement upon a domain ontology is typically useful for querying diverse data sets, but is insufficient for integrating neuroscience data spanning multiple domains. To this end, eNeuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of diverse types and levels of neuroscience data. We present a Semantic Web approach to building this e-Neuroscience data integration framework, which involves using RDF as a standard data model to facilitate representation and integration of data. We have converted a subset of the BrainPharm database into RDF and integrated it with SWAN hypothesis and publication data extracted from Alzforum and made available in RDF as the upper ontology. Our implementation uses the RDF Data Model in Oracle Database 10g for data retrieval, integration, and inference. Our approach should be generalizable across many types of biomedical information.

The Semantic Web has reached a level of maturity that allows RDF and OWL to be adopted by commercial software vendors. Products that incorporate these standards are being used to help provide solutions to the increasingly complex IT challenges that many industries face. Standardization efforts for the Semantic Web have progressed to the point where efforts are starting in the integration of ontologies and rules. This paper showcases the implementation of a Semantic Web rulebase in Oracle Database 10g, and Provides examples of its use within drug discovery and development. A more detailed paper is currently being prepared with Dr. Said Tabet of the RuleML initiative where a more detailed design and specification is provided explaining the.

Logic based systems typically use a collection of Condition -> Action rules to encode the behaviour and response of the system to a specific set of events. These systems function by having a working memory of facts. By adding, removing or changing facts in the working memory, the rules system can see which (if any) rules need triggering. If the effect of a rule changes a state in the working memory then rules will be re-evaluated.

New technologies have been developed in the life sciences that allow researchers to study biological systems in rich detail. These advances have resulted in an abundance of data that describes the relations between the fundamental components of biological systems, such as genes, proteins, and metabolites. The network of relations between the components holds insights as to how biological systems function, and consequently can help researchers understand the mechanisms behind disease. Biological networks are commonly managed and analyzed in a graph representation. Oracle Database 10g has the functionality to model data as a graph, and thereby has the potential to greatly facilitate research. In this paper we describe the Oracle implementation and provide case studies from the life sciences.

The recent trend towards enhanced functionality on the Web through rich internet applications has the ability to fundamentally change how people interact with each other. The most commonly used new applications include user-generated content, collaboration tools, shared bookmarks and social networks. Once the power of the Semantic Web is added to this environment, it will enable the currently isolated applications and silos of data to be connected together and it also will allow data to be represented in a machine readable way, thereby allowing inferencing capabilities. Technologies that are well suited for linking together data across the various social networks include FOAF (Friend of a Friend) and SIOC (Semantically-Interlinked Online communities). FOAF is a vocabulary that can be used to represent a profile about an individual, and can be used to link data from one social network to another (1). While SIOC can be used to aggregate data from various Web based media including Wikis, blogs, and newsfeeds; and to present information to users in the most appropriate representation (2). Social networks are being heavily explored by the health care and life sciences communities. A large reason for this likely stems from the significant usage of the Web for researching health related topics. Examples of social networking within this domain include Sermo which is a network of over 90,000 physicians in the USA who share experiences in treating patients (3); and 23andme that enables individuals who have had their SNP profile determined to see who else has a similar genetic makeup to them (4). Harvard University has recently won a significant grant from the National Institute of Health to implement social software to enable principal investigators within the university to better collaborate. Although there are many social networks across health care and life sciences, there are still opportunities for establishing communities for individuals who have particular diseases or who are taking specific medication. In these scenarios, significant value may be gained by linking together new and existing social networks. Work needs to be undertaken to better understand how to orchestrate the dynamic communities that need to capture, share, analyze, reuse and provide knowledge. The determination of patterns across the community in order to propose meaningful content back to individuals or communities needs exploration. There are also technical challenges relating to the

Motivation: Ontology development environments need to take advantage of scalable, reliable and secure data reposi- tories. This is becoming increasingly important as ontologies become larger in size and the number of simultaneous users grows. This paper describes the merits of integrating Pro- tégé with the RDF Data Model in the Oracle Database.