Joanne S. Luciano

The BioPAX community standard for pathway data sharing

Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

PAX of mind for pathway researchers

Scientists seeking to understand the inner workings of cells have access to a multitude of pathway data resources. However, the representations of pathway data within these resources are not consistent or interchangeable. To facilitate easy information retrieval from a wide variety of pathway resources, such as signal transduction, gene regulation, molecular interaction and metabolic pathway databases, a broad effort in the biopathways community called BioPAX was formed. New biological pathway software applications built using the BioPAX standard will be able to integrate knowledge from multiple sources in a coherent and reliable way. This article reports the progress that the BioPAX work-group has made towards building and deploying the BioPAX data-exchange format for biological pathway data.

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.

e-Science and biological pathway semantics

BackgroundThe development of e-Science presents a major set of opportunities and challenges for the future progress of biological and life scientific research. Major new tools are required and corresponding demands are placed on the high-throughput data generated and used in these processes. Nowhere is the demand greater than in the semantic integration of these data. Semantic Web tools and technologies afford the chance to achieve this semantic integration. Since pathway knowledge is central to much of the scientific research today it is a good test-bed for semantic integration. Within the context of biological pathways, the BioPAX initiative, part of a broader movement towards the standardization and integration of life science databases, forms a necessary prerequisite for its successful application of e-Science in health care and life science research. This paper examines whether BioPAX, an effort to overcome the barrier of disparate and heterogeneous pathway data sources, addresses the needs of e-Science.ResultsWe demonstrate how BioPAX pathway data can be used to ask and answer some useful biological questions. We find that BioPAX comes close to meeting a broad range of e-Science needs, but certain semantic weaknesses mean that these goals are missed. We make a series of recommendations for re-modeling some aspects of BioPAX to better meet these needs.ConclusionOnce these semantic weaknesses are addressed, it will be possible to integrate pathway information in a manner that would be useful in e-Science.

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.

The emergent discipline of health web science

The transformative power of the Internet on all aspects of daily life, including health care, has been widely recognized both in the scientific literature and in public discourse. Viewed through the various lenses of diverse academic disciplines, these transformations reveal opportunities realized, the promise of future advances, and even potential problems created by the penetration of the World Wide Web for both individuals and for society at large. Discussions about the clinical and health research implications of the widespread adoption of information technologies, including the Internet, have been subsumed under the disciplinary label of Medicine 2.0. More recently, however, multi-disciplinary research has emerged that is focused on the achievement and promise of the Web itself, as it relates to healthcare issues. In this paper, we explore and interrogate the contributions of the burgeoning field of Web Science in relation to health maintenance, health care, and health policy. From this, we introduce Health Web Science as a subdiscipline of Web Science, distinct from but overlapping with Medicine 2.0. This paper builds on the presentations and subsequent interdisciplinary dialogue that developed among Web-oriented investigators present at the 2012 Medicine 2.0 Conference in Boston, Massachusetts.

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.

Issues in learning an ontology from text

Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the animal behaviour domain. Our objective was to see how much could be done in a simple and relatively rapid manner using a corpus of journal papers. We used a sequence of pre-existing text processing steps, and here describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a number of hierarchies. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus.ResultsUsing mainly automated techniques, we were able to construct an 18055 term ontology-like structure with 73% recall of animal behaviour terms, but a precision of only 26%. We were able to clean unwanted terms from the nascent ontology using lexico-syntactic patterns that tested the validity of term inclusion within the ontology. We used the same technique to test for subsumption relationships between the remaining terms to add structure to the initially broad and shallow structure we generated. All outputs are available at http://thirlmere.aston.ac.uk/~kiffer/animalbehaviour/.ConclusionWe present a systematic method for the initial steps of ontology or structured vocabulary construction for scientific domains that requires limited human effort and can make a contribution both to ontology learning and maintenance. The method is useful both for the exploration of a scientific domain and as a stepping stone towards formally rigourous ontologies. The filtering of recognised terms from a heterogeneous corpus to focus upon those that are the topic of the ontology is identified to be one of the main challenges for research in ontology learning.

Dynamic enhancement of drug product labels to support drug safety, efficacy, and effectiveness

Out-of-date or incomplete drug product labeling information may increase the risk of otherwise preventable adverse drug events. In recognition of these concerns, the United States Federal Drug Administration (FDA) requires drug product labels to include specific information. Unfortunately, several studies have found that drug product labeling fails to keep current with the scientific literature. We present a novel approach to addressing this issue. The primary goal of this novel approach is to better meet the information needs of persons who consult the drug product label for information on a drug’s efficacy, effectiveness, and safety. Using FDA product label regulations as a guide, the approach links drug claims present in drug information sources available on the Semantic Web with specific product label sections. Here we report on pilot work that establishes the baseline performance characteristics of a proof-of-concept system implementing the novel approach. Claims from three drug information sources were linked to the Clinical Studies, Drug Interactions, and Clinical Pharmacology sections of the labels for drug products that contain one of 29 psychotropic drugs. The resulting Linked Data set maps 409 efficacy/effectiveness study results, 784 drug-drug interactions, and 112 metabolic pathway assertions derived from three clinically-oriented drug information sources (ClinicalTrials.gov, the National Drug File – Reference Terminology, and the Drug Interaction Knowledge Base) to the sections of 1,102 product labels. Proof-of-concept web pages were created for all 1,102 drug product labels that demonstrate one possible approach to presenting information that dynamically enhances drug product labeling. We found that approximately one in five efficacy/effectiveness claims were relevant to the Clinical Studies section of a psychotropic drug product, with most relevant claims providing new information. We also identified several cases where all of the drug-drug interaction claims linked to the Drug Interactions section for a drug were potentially novel. The baseline performance characteristics of the proof-of-concept will enable further technical and user-centered research on robust methods for scaling the approach to the many thousands of product labels currently on the market.

A semantic portal for next generation monitoring systems

We present a semantic technology-based approach to emerging monitoring systems based on our linked data approach in the Tetherless World Constellation Semantic Ecology and Environment Portal (SemantEco). Our integration scheme uses an upper level monitoring ontology and mid-level monitoring-relevant domain ontologies. The initial domain ontologies focus on water and air quality. We then integrate domain data from different authoritative sources and multiple regulation ontologies (capturing federal as well as state guidelines) to enable pollution detection and monitoring. An OWL-based reasoning scheme identifies pollution events relative to user chosen regulations. Our approach captures and leverages provenance to enable transparency. In addition, SemantEco features provenance-based facet generation, query answering, and validation over the integrated data via SPARQL. We introduce the general SemantEco approach, describe the implementation which has been built out substantially in the water domain creating the SemantAqua portal, and highlight some of the potential impacts for the future of semantically-enabled monitoring systems.