Semantic Similarity in Biomedical Ontologies

In recent years, ontologies have become a mainstream topic in biomedical research. When biological entities are described using a common schema, such as an ontology, they can be compared by means of their annotations. This type of comparison is called semantic similarity, since it assesses the degree of relatedness between two entities by the similarity in meaning of their annotations. The application of semantic similarity to biomedical ontologies is recent; nevertheless, several studies have been published in the last few years describing and evaluating diverse approaches. Semantic similarity has become a valuable tool for validating the results drawn from biomedical studies such as gene clustering, gene expression data analysis, prediction and validation of molecular interactions, and disease gene prioritization.     

PIDO: the primary immunodeficiency disease ontology

MOTIVATION: Primary immunodeficiency diseases (PIDs) are Mendelian conditions of high phenotypic complexity and low incidence. They usually manifest in toddlers and infants, although they can also occur much later in life. Information about PIDs is often widely scattered throughout the clinical as well as the research literature and hard to find for both generalists as well as experienced clinicians. Semantic Web technologies coupled to clinical information systems can go some way toward addressing this problem. Ontologies are a central component of such a system, containing and centralizing knowledge about primary immunodeficiencies in both a human- and computer-comprehensible form. The development of an ontology of PIDs is therefore a central step toward developing informatics tools, which can support the clinician in the diagnosis and treatment of these diseases. RESULTS: We present PIDO, the primary immunodeficiency disease ontology. PIDO characterizes PIDs in terms of the phenotypes commonly observed by clinicians during a diagnosis process. Phenotype terms in PIDO are formally defined using complex definitions based on qualities, functions, processes and structures. We provide mappings to biomedical reference ontologies to ensure interoperability with ontologies in other domains. Based on PIDO, we developed the PIDFinder, an ontology-driven software prototype that can facilitate clinical decision support. PIDO connects immunological knowledge across resources within a common framework and thereby enables translational research and the development of medical applications for the domain of immunology and primary immunodeficiency diseases.     

Biological data integration using Semantic Web technologies

Current research in biology heavily depends on the availability and efficient use of information. In order to build new knowledge, various sources of biological data must often be combined. Semantic Web technologies, which provide a common framework allowing data to be shared and reused between applications, can be applied to the management of disseminated biological data. However, due to some specificities of biological data, the application of these technologies to life science constitutes a real challenge. Through a use case of biological data integration, we show in this paper that current Semantic Web technologies start to become mature and can be applied for the development of large applications. However, in order to get the best from these technologies, improvements are needed both at the level of tool performance and knowledge modeling.     

Improving life sciences information retrieval using semantic web technology

The ability to retrieve relevant information is at the heart of every aspect of research and development in the life sciences industry. Information is often distributed across multiple systems and recorded in a way that makes it difficult to piece together the complete picture. Differences in data formats, naming schemes and network protocols amongst information sources, both public and private, must be overcome, and user interfaces not only need to be able to tap into these diverse information sources but must also assist users in filtering out extraneous information and highlighting the key relationships hidden within an aggregated set of information. The Semantic Web community has made great strides in proposing solutions to these problems, and many efforts are underway to apply Semantic Web techniques to the problem of information retrieval in the life sciences space. This article gives an overview of the principles underlying a Semantic Web-enabled information retrieval system: creating a unified abstraction for knowledge using the RDF semantic network model; designing semantic lenses that extract contextually relevant subsets of information; and assembling semantic lenses into powerful information displays. Furthermore, concrete examples of how these principles can be applied to life science problems including a scenario involving a drug discovery dashboard prototype called BioDash are provided.     

Biomedical ontologies: a functional perspective

The information explosion in biology makes it difficult for researchers to stay abreast of current biomedical knowledge and to make sense of the massive amounts of online information. Ontologies--specifications of the entities, their attributes and relationships among the entities in a domain of discourse--are increasingly enabling biomedical researchers to accomplish these tasks. In fact, bio-ontologies are beginning to proliferate in step with accruing biological data. The myriad of ontologies being created enables researchers not only to solve some of the problems in handling the data explosion but also introduces new challenges. One of the key difficulties in realizing the full potential of ontologies in biomedical research is the isolation of various communities involved: some workers spend their career developing ontologies and ontology-related tools, while few researchers (biologists and physicians) know how ontologies can accelerate their research. The objective of this review is to give an overview of biomedical ontology in practical terms by providing a functional perspective--describing how bio-ontologies can and are being used. As biomedical scientists begin to recognize the many different ways ontologies enable biomedical research, they will drive the emergence of new computer applications that will help them exploit the wealth of research data now at their fingertips.     

Knowledge networks in the age of the Semantic Web

The Web has become the major medium for various communities to share their knowledge. To this end, it provides an optimal environment for knowledge networks. The web offers global connectivity that is virtually instantaneous, and whose resources and documents can easily be indexed for easy searching. In the coupled realms of biomedical research and healthcare, this has become especially important where today many thousands of communities already exist that connect across academia, hospitals and industry. These communities also rely on several forms of knowledge assets, including publications, experimental data, domain-specific vocabularies and policies. Web-based communities will be one of the earlier beneficiaries of the emerging Semantic Web. With the new standards and technologies of the Semantic Web, effective utilization of knowledge networks will expand profoundly, fostering new levels of innovation and knowledge.     

Semantic Web Applications and Tools for the Life Sciences: SWAT4LS 2010

As Semantic Web technologies mature and new releases of key elements, such as SPARQL 1.1 and OWL 2.0, become available, the Life Sciences continue to push the boundaries of these technologies with ever more sophisticated tools and applications. Unsurprisingly, therefore, interest in the SWAT4LS (Semantic Web Applications and Tools for the Life Sciences) activities have remained high, as was evident during the third international SWAT4LS workshop held in Berlin in December 2010. Contributors to this workshop were invited to submit extended versions of their papers, the best of which are now made available in the special supplement of BMC Bioinformatics. The papers reflect the wide range of work in this area, covering the storage and querying of Life Sciences data in RDF triple stores, tools for the development of biomedical ontologies and the semantics-based integration of Life Sciences as well as clinicial data.     

Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online)

Background

An adequate and expressive ontological representation of biological organisms and their parts requires formal reasoning mechanisms for their relations of physical aggregation and containment.

Results

We demonstrate that the proposed formalism allows to deal consistently with "role propagation along non-taxonomic hierarchies", a problem which had repeatedly been identified as an intricate reasoning problem in biomedical ontologies.

Conclusion

The proposed approach seems to be suitable for the redesign of compositional hierarchies in (bio)medical terminology systems which are embedded into the framework of the OBO (Open Biological Ontologies) Relation Ontology and are using knowledge representation languages developed by the Semantic Web community.     

A top-level ontology of functions and its application in the Open Biomedical Ontologies

MOTIVATION: A clear understanding of functions in biology is a key component in accurate modelling of molecular, cellular and organismal biology. Using the existing biomedical ontologies it has been impossible to capture the complexity of the community's knowledge about biological functions. RESULTS: We present here a top-level ontological framework for representing knowledge about biological functions. This framework lends greater accuracy, power and expressiveness to biomedical ontologies by providing a means to capture existing functional knowledge in a more formal manner. An initial major application of the ontology of functions is the provision of a principled way in which to curate functional knowledge and annotations in biomedical ontologies. Further potential applications include the facilitation of ontology interoperability and automated reasoning. A major advantage of the proposed implementation is that it is an extension to existing biomedical ontologies, and can be applied without substantial changes to these domain ontologies. AVAILABILITY: The Ontology of Functions (OF) can be downloaded in OWL format from http://onto.eva.mpg.de/. Additionally, a UML profile and supplementary information and guides for using the OF can be accessed from the same website.     

Semantic similarity analysis of protein data: assessment with biological features and issues

The integration of proteomics data with biological knowledge is a recent trend in bioinformatics. A lot of biological information is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology). Annotating existing protein data with biological information may enable the use (and the development) of algorithms that use biological ontologies as framework to mine annotated data. Recently many methodologies and algorithms that use ontologies to extract knowledge from data, as well as to analyse ontologies themselves have been proposed and applied to other fields. Conversely, the use of such annotations for the analysis of protein data is a relatively novel research area that is currently becoming more and more central in research. Existing approaches span from the definition of the similarity among genes and proteins on the basis of the annotating terms, to the definition of novel algorithms that use such similarities for mining protein data on a proteome-wide scale. This work, after the definition of main concept of such analysis, presents a systematic discussion and comparison of main approaches. Finally, remaining challenges, as well as possible future directions of research are presented.     

COBrA: a bio-ontology editor

COBrA is a Java-based ontology editor for bio-ontologies that distinguishes itself from other editors by supporting the linking of concepts between two ontologies, and providing sophisticated analysis and verification functions. In addition to the Gene Ontology and Open Biology Ontologies formats, COBrA can import and export ontologies in the Semantic Web formats RDF, RDFS and OWL.     

A common layer of interoperability for biomedical ontologies based on OWL EL

MOTIVATION: Ontologies are essential in biomedical research due to their ability to semantically integrate content from different scientific databases and resources. Their application improves capabilities for querying and mining biological knowledge. An increasing number of ontologies is being developed for this purpose, and considerable effort is invested into formally defining them in order to represent their semantics explicitly. However, current biomedical ontologies do not facilitate data integration and interoperability yet, since reasoning over these ontologies is very complex and cannot be performed efficiently or is even impossible. We propose the use of less expressive subsets of ontology representation languages to enable efficient reasoning and achieve the goal of genuine interoperability between ontologies. RESULTS: We present and evaluate EL Vira, a framework that transforms OWL ontologies into the OWL EL subset, thereby enabling the use of tractable reasoning. We illustrate which OWL constructs and inferences are kept and lost following the conversion and demonstrate the performance gain of reasoning indicated by the significant reduction of processing time. We applied EL Vira to the open biomedical ontologies and provide a repository of ontologies resulting from this conversion. EL Vira creates a common layer of ontological interoperability that, for the first time, enables the creation of software solutions that can employ biomedical ontologies to perform inferences and answer complex queries to support scientific analyses. Availability and implementation: The EL Vira software is available from http://el-vira.googlecode.com and converted OBO ontologies and their mappings are available from http://bioonto.gen.cam.ac.uk/el-ont.     

Alzforum and SWAN: the present and future of scientific web communities

Scientists drove the early development of the World Wide Web,primarily as a means for rapid communication, document sharingand data access. They have been far slower to adopt the webas a medium for building research communities. Yet, web-basedcommunities hold great potential for accelerating the pace ofscientific research. In this article, we will describe the 10-yearexperience of the Alzheimer Research Forum (‘Alzforum’),a unique example of a thriving scientific web community, andexplain the features that contributed to its success. We willthen outline the SWAN (Semantic Web Applications in Neuromedicine)project, in which Alzforum curators are collaborating with informaticsresearchers to develop novel approaches that will enable communitiesto share richly contextualized information about scientificdata, claims and hypotheses.      

Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning

Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect to automated reasoning for large scale data integration and knowledge discovery. We describe a method to improve automated reasoning over biomedical ontologies and identify several thousand contradictory class definitions. Our approach aligns terms in biomedical ontologies with foundational classes in a top-level ontology and formalizes composite relations as class expressions. We describe the semi-automated repair of contradictions and demonstrate expressive queries over interoperable ontologies. Our work forms an important cornerstone for data integration, automatic inference and knowledge discovery based on formal representations of knowledge. Our results and analysis software are available at http://bioonto.de/pmwiki.php/Main/ReasonableOntologies.     

National Center for Biomedical Ontology: advancing biomedicine through structured organization of scientific knowledge

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease.     

Life Sciences and the web: a new era for collaboration

The World Wide Web has revolutionized how researchers from various disciplines collaborate over long distances. This is nowhere more important than in the Life Sciences, where interdisciplinary approaches are becoming increasingly powerful as a driver of both integration and discovery. Data access, data quality, identity, and provenance are all critical ingredients to facilitate and accelerate these collaborative enterprises and it is here where Semantic Web technologies promise to have a profound impact. This paper reviews the need for, and explores advantages of as well as challenges with these novel Internet information tools as illustrated with examples from the biomedical community.     

Automatic Background Knowledge Selection for Matching Biomedical Ontologies

Ontology matching is a growing field of research that is of critical importance for the semantic web initiative. The use of background knowledge for ontology matching is often a key factor for success, particularly in complex and lexically rich domains such as the life sciences. However, in most ontology matching systems, the background knowledge sources are either predefined by the system or have to be provided by the user. In this paper, we present a novel methodology for automatically selecting background knowledge sources for any given ontologies to match. This methodology measures the usefulness of each background knowledge source by assessing the fraction of classes mapped through it over those mapped directly, which we call the mapping gain. We implemented this methodology in the AgreementMakerLight ontology matching framework, and evaluate it using the benchmark biomedical ontology matching tasks from the Ontology Alignment Evaluation Initiative (OAEI) 2013. In each matching problem, our methodology consistently identified the sources of background knowledge that led to the highest improvements over the baseline alignment (i.e., without background knowledge). Furthermore, our proposed mapping gain parameter is strongly correlated with the F-measure of the produced alignments, thus making it a good estimator for ontology matching techniques based on background knowledge.     

Physical properties of biological entities: an introduction to the ontology of physics for biology

As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities-molecules, cells, organs-are well-established, there are no principled ontologies of physical properties-energies, volumes, flow rates-of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB), a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration.