Ontology-Based Queries over Cancer Data

The ever-increasing amount of data in biomedical research, and in cancer research in particular, needs to be managed to support efficient data access, exchange and integration. Existing software infrastructures, such as caGrid, support access to distributed information annotated with a domain ontology. However, caGrid's current querying functionality depends on the structure of individual data resources without exploiting the semantic annotations. In this paper, we present the design and development of an ontology-based querying functionality that consists of: the generation of OWL2 ontologies from the underlying data resources’ metadata and a query rewriting and translation process based on reasoning, which converts a query at the domain ontology level into queries at the software infrastructure level. We present a detailed analysis of our approach as well as an extensive performance evaluation. While the implementation and evaluation was performed for the caGrid infrastructure, the approach could be applicable to other model and metadata-driven environments for data sharing

Exploiting biomedical web resources: a case study

An increasing number of web resources continue to be extensively used by healthcare operators to obtain more accurate diagnostic results. In particular, health care is reaping the benefits of technological advances in genomic for facing the demand of genetic tests that allow a better comprehension of diagnostic results. Within this context, Gene Ontology (GO) is a popular and effective mean for extracting knowledge from a list of genes and evaluating their semantic similarity. This paper investigates about the potential and any limits of GO ontology as support for capturing information about a set of genes which are supposed to play a significant role in a pathological condition. In particular, we present a case study that exploits some biomedical web resources for devising several groups of functionally coherent genes and experiments about the evaluation of their semantic similarity over GO. Due to the GO structure and content, results reveal limitations that not affect the evaluation of the semantic similarity when genes exhibit simple correlations but influence the estimation of the relatedness of genes belonging to complex organizations

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

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

Developing the Quantitative Histopathology Image Ontology : A case study using the hot spot detection problem

Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology – QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts

Developing Ontological Background Knowledge for Biomedicine

Biomedicine is an impressively fast developing, interdisciplinary field of research. To control the growing volumes of biomedical data, ontologies are increasingly used as common organization structures. Biomedical ontologies describe domain knowledge in a formal, computationally accessible way. They serve as controlled vocabularies and background knowledge in applications dealing with the integration, analysis and retrieval of heterogeneous types of data. The development of biomedical ontologies, however, is hampered by specific challenges. They include the lack of quality standards, resulting in very heterogeneous resources, and the decentralized development of biomedical ontologies, causing the increasing fragmentation of domain knowledge across them. In the first part of this thesis, a life cycle model for biomedical ontologies is developed, which is intended to cope with these challenges. It comprises the stages "requirements analysis", "design and implementation", "evaluation", "documentation and release" and "maintenance". For each stage, associated subtasks and activities are specified. To promote quality standards for biomedical ontology development, an emphasis is set on the evaluation stage. As part of it, comprehensive evaluation procedures are specified, which allow to assess the quality of ontologies on various levels. To tackle the issue of knowledge fragmentation, the life cycle model is extended to also cover ontology alignments. Ontology alignments specify mappings between related elements of different ontologies. By making potential overlaps and similarities between ontologies explicit, they support the integration of ontologies and help reduce the fragmentation of knowledge. In the second part of this thesis, the life cycle model for biomedical ontologies and alignments is validated by means of five case studies. As a result, they confirm that the model is effective. Four of the case studies demonstrate that it is able to support the development of useful new ontologies and alignments. The latter facilitate novel natural language processing and bioinformatics applications, and in one case constitute the basis of a task of the "BioNLP shared task 2013", an international challenge on biomedical information extraction. The fifth case study shows that the presented evaluation procedures are an effective means to check and improve the quality of ontology alignments. Hence, they support the crucial task of quality assurance of alignments, which are themselves increasingly used as reference standards in evaluations of automatic ontology alignment systems. Both, the presented life cycle model and the ontologies and alignments that have resulted from its validation improve information and knowledge management in biomedicine and thus promote biomedical research

Applications of the ACGT Master Ontology on Cancer

In this paper we present applications of the ACGT Master Ontology (MO) which is a new terminology resource for a transnational network providing data exchange in oncology, emphasizing the integration of both clinical and molecular data. The development of a new ontology was necessary due to problems with existing biomedical ontologies in oncology. The ACGT MO is a test case for the application of best practices in ontology development. This paper provides an overview of the application of the ontology within the ACGT project thus far

Ontology-based knowledge representation of experiment metadata in biological data mining

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes

Constructing a lattice of Infectious Disease Ontologies from a Staphylococcus aureus isolate repository

A repository of clinically associated Staphylococcus aureus (Sa) isolates is used to semi‐automatically generate a set of application ontologies for specific subfamilies of Sa‐related disease. Each such application ontology is compatible with the Infectious Disease Ontology (IDO) and uses resources from the Open Biomedical Ontology (OBO) Foundry. The set of application ontologies forms a lattice structure beneath the IDO‐Core and IDO‐extension reference ontologies. We show how this lattice can be used to define a strategy for the construction of a new taxonomy of infectious disease incorporating genetic, molecular, and clinical data. We also outline how faceted browsing and query of annotated data is supported using a lattice application ontology