Susie Stephens

Linked open drug data for pharmaceutical research and development

There is an abundance of information about drugs available on the Web. Data sources range from medicinal chemistry results, over the impact of drugs on gene expression, to the outcomes of drugs in clinical trials. These data are typically not connected together, which reduces the ease with which insights can be gained. Linking Open Drug Data (LODD) is a task force within the World Wide Web Consortiums (W3C) Health Care and Life Sciences Interest Group (HCLS IG). LODD has surveyed publicly available data about drugs, created Linked Data representations of the data sets, and identified interesting scientific and business questions that can be answered once the data sets are connected. The task force provides recommendations for the best practices of exposing data in a Linked Data representation. In this paper, we present past and ongoing work of LODD and discuss the growing importance of Linked Data as a foundation for pharmaceutical R&D data sharing.

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside

BackgroundTranslational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery.ResultsWe developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records. We demonstrate the use of Semantic Web technologies in the integration of patient and biomedical data, and reveal how such a knowledge base can aid physicians in providing tailored patient care and facilitate the recruitment of patients into active clinical trials. Thus, patients, physicians and researchers may explore the knowledge base to better understand therapeutic options, efficacy, and mechanisms of action.ConclusionsThis work takes an important step in using Semantic Web technologies to facilitate integration of relevant, distributed, external sources and progress towards a computational platform to support personalized medicine.AvailabilityTMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql.

Semantic Web for Health Care and Life Sciences: a review of the state of the art

Biomedical researchers need to be able to ask questions that span many heterogeneous data sources in order to make well-informed decisions that may lead to important scientific breakthroughs. For this to be achieved, diverse types of data about drugs, patients, diseases, proteins, cells, pathways and so on must be effectively integrated. Yet, linking disparate biomedical data continues to be a challenge due to inconsistency in naming and heterogeneity in data models and formats. Many organizations are now exploring the use of Semantic Web technologies in the hope of easing the cost of data integration [1]. The benefits promised by the Semantic Web include integration of heterogeneous data using explicit semantics, simplified annotation and sharing of findings, rich explicit models for data representation, aggregation and search, easier re-use of data in unanticipated ways, and the application of logic to infer additional information [2]. The World Wide Web Consortium (W3C) (http://www.w3.org/) has established the Semantic Web for Health Care and Life Sciences Interest Group (HCLS IG) (http://www.w3.org/2001/sw/hcls/) to help organizations in their adoption of the Semantic Web. The HCLS IG is chartered to develop and support the use of Semantic Web technologies to improve collaboration, research and development, innovation, and adoption in the domains of Health Care and Life Sciences. As a part of realizing this vision, a workshop on the Semantic Web for Health Care and Life Sciences was organized in conjunction with WWW2008 (http://esw.w3.org/topic/HCLS/WWW2008) [3]. The workshop provided a review of the latest positions and research in this domain. Five of the seven papers within this issue originated from the HCLS/WWW2008 workshop and review a range of Semantic Web technologies/approaches employed in different biomedical domains. Vandervalk et al. describe ‘The State of the Union’ for the adoption of Semantic Web standards by key institutes in bioinformatics. The paper explores the nature and connectivity of several community-driven semantic warehousing projects. It reports on the progress with the CardioSHARE/Moby-2 project, which aims to make the resources of the ‘Deep Web’ transparently accessible through SPARQL queries. It points out that the warehouse approach is limited, in that queries are confined to the resources that have been selected for inclusion. It also discusses a related problem that the majority of bioinformatics data exist in the ‘Deep Web’, that is, the data does not exist until an application or analytical tool is invoked, and therefore does not have a predictable Web address. It also highlights that the inability to utilize Uniform Resource Identifiers (URIs) to address bioinformatics data is a barrier to its accessibility in the Semantic Web. Das et al. discuss the use of ontologies to bridge diverse Web-based communities. The paper introduces the Science Collaboration Framework (SCF) as a reusable platform for advanced online collaboration in biomedical research. SCF supports structured Web 2.0 community discourse amongst researchers, makes heterogeneous data resources available to collaborating scientists, captures the semantics of the relationships between resources, and structures discourse around the resources. The first instance of the SCF framework is being used to create an open-access online community for stem cell research—StemBook (http://www.stembook.org). The SCF framework has been applied to interdisciplinary areas such as neurodegenerative disease and neuro-repair research, but has broad utility across the natural sciences. Zhao et al. describe various design patterns for representing and querying provenance information relating to mapping links between heterogeneous data from sources in the domain of functional genomics. The paper illustrates the use of named RDF graphs at different levels of granularity to make provenance assertions about linked data. It also demonstrates that these assertions are sufficient to support requirements including data currency, integrity, evidential support and historical queries. Dumontier et al. discuss a number of approaches for capturing pharmacogenomic data and other related information to facilitate data sharing and knowledge discovery. The paper describes how recent advances in Semantic Web technologies have presented exciting new opportunities for knowledge discovery related to pharmacogenomics by representing information with machine-understandable semantics. It illustrates progress in this area with respect to a personalized medicine project which aims to facilitate pharmacogenomics knowledge discovery through intuitive knowledge capture and sophisticated question answering using automated reasoning over expressive ontologies. Manning et al. review several data integration approaches that involve extracting data from a wide variety of public and private data repositories, each of which is associated with a unique vocabulary and schema. The paper presents an implemented data architecture that leverages semantic mapping of experimental metadata to support the rapid development of scientific discovery applications. This achieves the twin goals of reducing architectural complexity while leveraging Semantic Web technologies to provide flexibility, efficiency and more fully characterized data relationships. The architecture consists of a metadata ontology, a metadata repository and an interface that allows access to the repository. The paper describes how this approach allows scientists to discover and link relevant data across diverse data sources. It provides a platform for development of integrative informatics applications. Chen et al. survey the feasibility and state of the art for using Semantic Web technology to represent, integrate and analyze knowledge in a range of biomedical networks. The paper introduces a conceptual framework to enable researchers to integrate graph mining with ontology reasoning in network data analysis. Four case studies are used to demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology (GO) category cross-talks, drug efficacy analysis and herb–drug interaction analysis. Ruttenberg et al. review the use of Semantic Web technologies for assembling and querying biomedical knowledge from multiple sources and disciplines. The paper presents the Neurocommons prototype knowledge base, a demonstration intended to show the feasibility and benefits of using Semantic Web technologies. The prototype allows one to explore the scalability of current Semantic Web tools and methods for creating such a resource, and to reveal issues that will need to be addressed in order to further expand its scope and use. The paper demonstrates the utility of the knowledge base by reviewing a few example queries that provide answers to precise questions relevant to the understanding of the disease.

AlzPharm: integration of neurodegeneration data using RDF

BackgroundNeuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data.ResultsWe have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion.ConclusionAccessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results suggest that our Semantic Web approach is suitable for realizing e-Neuroscience and generic enough to be applied in other biomedical fields.

Aggregation of bioinformatics data using Semantic Web technology

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.

Applying semantic Web technologies to drug safety determination

Ensuring drug safety is of paramount importance to the life sciences industry. Its critical that drugs are able not only to achieve the desired result but also to do so without harmful side effects. By identifying problems as early as possible in the drug discovery and development process, life sciences companies can avoid drug safety sagas, such as a recent example that concerned COX-2 inhibitors. Unfortunately, drug safety problems are often revealed only during clinical trials or occasionally after marketing. These challenges are becoming more acute as medicines are targeted to defined patient populations. The life sciences industry can use semantic Web technologies to integrate data more effectively across all drug discovery and development business units, thereby providing a more supportive environment for the early detection of safety-related issues. Effective integration would enable genomic data and patient profiles to be more easily related to safety, thus providing: 1) a simpler framework for determining risk-benefit for individual patients in particular treatment regimens, 2) a better mechanism to distribute new data relating to safety throughout the organization, and 3) a better decision-making environment to determine which drugs to pursue. Furthermore, semantic Web inferencing capabilities enable an intelligent decision support system or autonomous agent to reason about combined domain-specific and industry-specific knowledge and act on the conclusions drawn from this inferencing process.

Guest Editorial: Semantic mashup of biomedical data

As the diversity and quantity of Web-accessible data in the biomedical domain grow, there are increasing benefits in empowering end-user scientists, working on their own, to integrate the various sources of data. Traditionally, significant programming effort has been required to parse and integrate heterogeneous datasets prior to enabling scientists to answer interesting questions. The heterogeneity includes different data formats, information models, and terminologies. Recently, a new breed of Web-based data-integration tools has been developed to simplify this process. They are called “mashups.” These mashup tools have been designed to empower end-users to be able to extract, format, and remix data across multiple Web sites. Examples of such tools include Dapper (http://www.dapper.net/), which allows users to extract/scrape data from Web pages visually and to produce the extracted data as feeds in formats such as Rich Site Summary (RSS) (http://web.resource.org/rss/1.0/spec); Google Maps (http://maps.google.com), which provides the ability to mashup (integrate) datasets in the Keyhole Markup Language (KML) format and to visualize the integrated results; and Yahoo! Pipes (http://pipes.yahoo.com/pipes/), which provides operators/widgets to mashup heterogeneously formatted datasets (e.g., tabular, RSS, and KML formats). In addition to accessing user-friendly mashup tools, Web programmers can directly use open Web APIs, such as those listed in ProgrammableWeb (http://www.programmableweb.com/). Mashup tools have been designed to allow disparate data sources to be brought together to increase utility to end-users. However, even with the tools and open APIs, users must perform most of the system integration. There is a need for creating mashups that better enable computers to help people achieve more powerful and complex data integration involving semantic mappings across multiple information models, terminologies, and ontologies. The term for such machine-based integration of data is “semantic mashups.” The transition to semantic mashups is made possible using Semantic Web technology (http://www.w3.org/2001/sw/), which facilitates the sharing of the meaning of data. This in turn makes it much easier to combine the stovepipe systems and to integrate data in new and unexpected ways. The key components of the Semantic Web include RDF as the basic data model, OWL for expressive ontologies, and SPARQL for query. This special issue highlights the transition from mashups to semantic mashups in the context of biomedicine. At the American Medical Informatics Association’s Annual Symposium in 1998 (AMIA98), Sir Tim Berners-Lee gave the keynote speech on the role of the Web in the information-intensive era of health care and biomedical research. In his speech, Berners-Lee envisioned the transition of the Web from being human-oriented to being increasingly machine-friendly. This burgeoning vision of the machine-friendly Web later became the Semantic Web vision. Since the seminal publication on the Semantic Web in Scientific American in 2001 [1], the Semantic Web has progressed from being a vision to reality [2], although we still have some way to go before reaching the most futuristic aspects of the original Scientific American article. Adoption of the Semantic Web has been especially evident within health care and life sciences. In part, this has been driven by the World Wide Web Consortium (W3C), which created an interest group focused on the application of the Semantic Web to this domain area (http://www.w3.org/2001/sw/hcls/). The group has been chartered to develop and support the use of Semantic Web technologies and practices to improve collaboration, research and development, and innovation adoption in health care and the life sciences. Increased adoption has been observed in the form of increasing numbers of academic papers, special issues in journals (e.g., [3]), books (e.g., [4]), and conferences (e.g., [5]). An increasing number of implementations within commercial enterprises have also been documented (http://www.w3.org/2001/sw/sweo/public/UseCases/). The annual World Wide Web (WWW) conference is one of the world’s largest meetings for Web researchers, practitioners, and developers. A workshop titled “Health Care and Life Sciences Data Integration for the Semantic Web” (http://www2007.org/workshop-W2.php) was co-located with the WWW2007 conference. While Berners-Lee’s AMIA keynote speech introduced the nascent vision of the Semantic Web to the biomedical informatics community, the workshop at WWW2007 provided concrete examples of how both academic and commercial organizations are embracing the technology. A number of the papers in this special issue of JBI originated at, and are expanded from, the workshop, while other papers were selected from submissions responding to the issue’s public call for papers. The aim of this special issue is to raise awareness of the benefits of using Semantic Web technology for data integration within health care and life sciences. The following section outlines the organization of this special issue and gives a brief introduction to the papers.

Enabling semantic web inferencing with oracle technology: applications in life sciences

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

Selected papers from the 12th annual Bio-Ontologies meeting

Soldatova, L. N., Lord, P., Sansone, S., Stephens, S. M., Shah, N. H. (2010). Selected papers from the 12th annual Bio-Ontologies meeting. Journal of Biomedical Semantics, 1 (Suppl 1), [I1].

Integrating scientific data for drug discovery and development using the Life Sciences Grid

Background: There are many daunting challenges for companies who wish to bring novel drugs to market. The information complexity around potential drug targets has increased greatly with the introduction of microarrays, high-throughput screening and other technological advances over the past decade, but has not yet fundamentally increased our understanding of how to modify a disease with pharmaceuticals. Further, the bar has been raised in getting a successful drug to market as just being new is no longer enough: the drug must demonstrate improved performance compared with the ever increasing generic pharmacopeia to gain support from payers and government authorities. In addition, partly as a consequence of a climate of concern regarding the safety of drugs, regulatory authorities have approved fewer new molecular entities compared to historical norms over the past few years. Objective: To overcome these challenges, the pharmaceutical industry must fully embrace information technology to bring better understood compounds to market. An important first step in addressing an unmet medical need is in understanding the disease and identifying the physiological target(s) to be modulated by the drug. Deciding which targets to pursue for a given disease requires a multidisciplinary effort that integrates heterogeneous data from many sources, including genetic variations of populations, changes in gene expression and biochemical assays. Method: The Life Science Grid was developed to provide a flexible framework to integrate such diverse biological, chemical and disease information to help scientists make better-informed decisions. Results/conclusion: The Life Science Grid has been used to rapidly and effectively integrate scientific information in the pharmaceutical industry and has been placed in the open source community to foster collaboration in the life sciences community.