Bio-Ontology and text: bridging the modeling gap

MOTIVATION: Natural language processing (NLP) techniques are increasingly being used in biology to automate the capture of new biological discoveries in text, which are being reported at a rapid rate. Yet, information represented in NLP data structures is classically very different from information organized with ontologies as found in model organisms or genetic databases. To facilitate the computational reuse and integration of information buried in unstructured text with that of genetic databases, we propose and evaluate a translational schema that represents a comprehensive set of phenotypic and genetic entities, as well as their closely related biomedical entities and relations as expressed in natural language. In addition, the schema connects different scales of biological information, and provides mappings from the textual information to existing ontologies, which are essential in biology for integration, organization, dissemination and knowledge management of heterogeneous phenotypic information. A common comprehensive representation for otherwise heterogeneous phenotypic and genetic datasets, such as the one proposed, is critical for advancing systems biology because it enables acquisition and reuse of unprecedented volumes of diverse types of knowledge and information from text. RESULTS: A novel representational schema, PGschema, was developed that enables translation of phenotypic, genetic and their closely related information found in textual narratives to a well-defined data structure comprising phenotypic and genetic concepts from established ontologies along with modifiers and relationships. Evaluation for coverage of a selected set of entities showed that 90% of the information could be represented (95% confidence interval: 86-93%; n = 268). Moreover, PGschema can be expressed automatically in an XML format using natural language techniques to process the text. To our knowledge, we are providing the first evaluation of a translational schema for NLP that contains declarative knowledge about genes and their associated biomedical data (e.g. phenotypes). AVAILABILITY: http://zellig.cpmc.columbia.edu/PGschema     

Information retrieval and knowledge discovery utilising a biomedical Semantic Web

Although various ontologies and knowledge sources have been developed in recent years to facilitate biomedical research, it is difficult to assimilate information from multiple knowledge sources. To enable researchers to easily gain understanding of a biomedical concept, a biomedical Semantic Web that seamlessly integrates knowledge from biomedical ontologies, publications and patents would be very helpful. In this paper, current research efforts in representing biomedical knowledge in Semantic Web languages are surveyed. Techniques are presented for information retrieval and knowledge discovery from the Semantic Web that extend traditional keyword search and database querying techniques. Finally, some of the challenges that have to be addressed to make the vision of a biomedical Semantic Web a reality are discussed.     

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.     

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.     

The future role of bio-ontologies for developing a general data standard in biology: chance and challenge for zoo-morphology

Due to lack of common data standards, the communicability and comparability of biological data across various levels of organization and taxonomic groups is continuously decreasing. However, the interdependence between molecular and higher levels of organization is of growing interest and calls for co-operations between biologists from different methodological and theoretical backgrounds. A general data standard in biology would greatly facilitate such co-operations. This article examines the role that defined and formalized vocabularies (i.e., ontologies) could have in developing such a data standard. I suggest basic criteria for developing data standards on grounds of distinguishing content, concept, nomenclatural, and format standards and discuss the role of data bases and their use of bio-ontologies in current activities for data standardization in biology. General principles of ontology development are introduced, including foundational ontology properties (e.g. class–subclass, parthood), and how concepts are defined. After addressing problems that are specific to morphological data, the notion of a general structure concept for morphology is introduced and why it is required for developing a morphological ontology. The necessity for a general morphological ontology to be taxon-independent and free of homology assumptions is discussed and how it can solve the problems of morphology. The article concludes with an outlook on how the use of ontologies will likely establish some sort of general data standard in biology and why the development of a set of commonly used foundational ontology properties and the use of globally unique identifiers for all classes defined in ontologies is crucial for its success.     

Identification of key concepts in biomedical literature using a modified Markov heuristic

MOTIVATION: The recent explosion of interest in mining the biomedical literature for associations between defined entities such as genes, diseases and drugs has made apparent the need for robust methods of identifying occurrences of these entities in biomedical text. Such concept-based indexing is strongly dependent on the availability of a comprehensive ontology or lexicon of biomedical terms. However, such ontologies are very difficult and expensive to construct, and often require extensive manual curation to render them suitable for use by automatic indexing programs. Furthermore, the use of statistically salient noun phrases as surrogates for curated terminology is not without difficulties, due to the lack of high-quality part-of-speech taggers specific to medical nomenclature. RESULTS: We describe a method of improving the quality of automatically extracted noun phrases by employing prior knowledge during the HMM training procedure for the tagger. This enhancement, when combined with appropriate training data, can greatly improve the quality and relevance of the extracted phrases, thereby enabling greater accuracy in downstream literature mining tasks.     

Process attributes in bio-ontologies

ABSTRACT: BACKGROUND: Biomedical processes can provide essential information about the (mal-) functioning of an organism and are thus frequently represented in biomedical terminologies and ontologies, including the GO Biological Process branch. These processes often need to be described and categorised in terms of their attributes, such as rates or regularities. The adequate representation of such process attributes has been a contentious issue in bio-ontologies recently; and domain ontologies have correspondingly developed ad hoc workarounds that compromise interoperability and logical consistency. RESULTS: We present a design pattern for the representation of process attributes that is compatible with upper ontology frameworks such as BFO and BioTop. Our solution rests on two key tenets: firstly, that many of the sorts of process attributes which are biomedically interesting can be characterised by the ways that repeated parts of such processes constitute, in combination, an overall process; secondly, that entities for which a full logical definition can be assigned do not need to be treated as primitive within a formal ontology framework. We apply this approach to the challenge of modelling and automatically classifying examples of normal and abnormal rates and patterns of heart beating processes, and discuss the expressivity required in the underlying ontology representation language. We provide full definitions for process attributes at increasing levels of domain complexity. CONCLUSIONS: We show that a logical definition of process attributes is feasible, though limited by the expressivity of DL languages so that the creation of primitives is still necessary. This finding may endorse current formal upper-ontology frameworks as a way of ensuring consistency, interoperability and clarity.     

On carcinomas and other pathological entities

Tumours, abscesses, cysts, scars and fractures are familiar types of what we shall call pathological continuant entities. The instances of such types exist always in or on anatomical structures, which thereby become transformed into pathological anatomical structures of corresponding types: a fractured tibia, a blistered thumb, a carcinomatous colon. In previous work on biomedical ontologies we showed how the provision of formal definitions for relations such as is_a, part_of and transformation_of can facilitate the integration of such ontologies in ways which have the potential to support new kinds of automated reasoning. We here extend this approach to the treatment of pathologies, focusing especially on those pathological continuant entities which arise when organs become affected by carcinomas.     

Biomedical informatics and granularity

An explicit formal-ontological representation of entities existing at multiple levels of granularity is an urgent requirement for biomedical information processing. We discuss some fundamental principles which can form a basis for such a representation. We also comment on some of the implicit treatments of granularity in currently available ontologies and terminologies (GO, FMA, SNOMED CT).     

The use of concept maps during knowledge elicitation in ontology development processes – the nutrigenomics use case

Background  

Incorporation of ontologies into annotations has enabled 'semantic integration' of complex data, making explicit the knowledge within a certain field. One of the major bottlenecks in developing bio-ontologies is the lack of a unified methodology. Different methodologies have been proposed for different scenarios, but there is no agreed-upon standard methodology for building ontologies. The involvement of geographically distributed domain experts, the need for domain experts to lead the design process, the application of the ontologies and the life cycles of bio-ontologies are amongst the features not considered by previously proposed methodologies.     

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.     

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.     

The Seventh Annual Bio-Ontologies Meeting Moat House Hotel, Glasgow, 30 July 2004

The Annual Bio-Ontologies Meeting has now reached its seventh consecutive year, running as a special interest group (SIG) of the much larger ISMB conference. This year's meeting in Glasgow had approximately 100 attendees. Since the advent of the Gene Ontology, which coincided with the first Bio-Ontologies Meeting, we have seen a year-on-year strengthening of the field; bio-ontologies has moved from being dominated by computer science to be led by biological applications; discussion is less about 'what is an ontology?' and more about 'how to build an ontology which is fit for purpose?'. This strengthening of the field can be seen elsewhere. Both the main ISMB conference and this year's Pacific Symposium on Biocomputing (PSB) have seen a large number of submissions to their ontologies track. For the first time a selection of the papers from the SIG is being published in this issue of Comparative and Functional Genomics. We hope that this will complement the publications of the larger conferences, bringing to a wider audience the cutting edge research that characterizes the Bio-Ontologies SIG.     

Ontologies: Formalising biological knowledge for bioinformatics

An ontology is a domain of knowledge structured through formal rules so that it can be interpreted and used by computers. Ontologies are becoming increasingly important in bioinformatics because they can be linked to the information in databases and their knowledge then used to query the databases. Typical examples in current use are the Gene Ontology, which incorporates much of our knowledge about gene products, and ontologies of developmental anatomy, which, for example, facilitate tissue-based queries to gene expression databases both textually and spatially. This article considers the production, formulation and types of bio-ontologies together with the reasons why they are so useful.     

The Ontology for Biomedical Investigations

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.