Anika Groß

Impact of ontology evolution on functional analyses

MOTIVATION Ontologies are used in the annotation and analysis of biological data. As knowledge accumulates, ontologies and annotation undergo constant modifications to reflect this new knowledge. These modifications may influence the results of statistical applications such as functional enrichment analyses that describe experimental data in terms of ontological groupings. Here, we investigate to what degree modifications of the Gene Ontology (GO) impact these statistical analyses for both experimental and simulated data. The analysis is based on new measures for the stability of result sets and considers different ontology and annotation changes. RESULTS Our results show that past changes in the GO are non-uniformly distributed over different branches of the ontology. Considering the semantic relatedness of significant categories in analysis results allows a more realistic stability assessment for functional enrichment studies. We observe that the results of term-enrichment analyses tend to be surprisingly stable despite changes in ontology and annotation.

Semi-automatic Adaptation of Mappings between Life Science Ontologies

The continuous evolution of life science ontologies requires the adaptation of their associated mappings. We propose two approaches for tackling this problem in a largely automatic way: (1) a composition-based adaptation relying on the principle of mapping composition and (2) a diff-based adaptation algorithm individually handling change operations to update the mapping. Both techniques reuse unaffected correspondences, and adapt only the affected mapping part. We experimentally assess and confirm the effectiveness of our approaches for evolving mappings between large life science ontologies.

Efficient Management of Biomedical Ontology Versions

Ontologies have become very popular in life sciences and other domains. They mostly undergo continuous changes and new ontology versions are frequently released. However, current analysis studies do not consider the ontology changes reflected in different versions but typically limit themselves to a specific ontology version which may quickly become obsolete. To allow applications easy access to different ontology versions we propose a central and uniform management of the versions of different biomedical ontologies. The proposed database approach takes concept and structural changes of succeeding ontology versions into account thereby supporting different kinds of change analysis. Furthermore, it is very space-efficient by avoiding redundant storage of ontology components which remain unchanged in different versions. We evaluate the storage requirements and query performance of the proposed approach for the Gene Ontology.

Evolution of biomedical ontologies and mappings: Overview of recent approaches.

Biomedical ontologies are heavily used to annotate data, and different ontologies are often interlinked by ontology mappings. These ontology-based mappings and annotations are used in many applications and analysis tasks. Since biomedical ontologies are continuously updated dependent artifacts can become outdated and need to undergo evolution as well. Hence there is a need for largely automated approaches to keep ontology-based mappings up-to-date in the presence of evolving ontologies. In this article, we survey current approaches and novel directions in the context of ontology and mapping evolution. We will discuss requirements for mapping adaptation and provide a comprehensive overview on existing approaches. We will further identify open challenges and outline ideas for future developments.

Optimizing Similarity Computations for Ontology Matching - Experiences from GOMMA

An efficient computation of ontology mappings requires optimized algorithms and significant computing resources especially for large life science ontologies. We describe how we optimized n-gram matching for computing the similarity of concept names and synonyms in our match system GOMMA. Furthermore, we outline how to enable a highly parallel string matching on Graphical Processing Units (GPU). The evaluation on the OAEI LargeBio match task demonstrates the high effectiveness of the proposed optimizations and that the use of GPUs in addition to standard processors enables significant performance improvements.

A multi-part matching strategy for mapping LOINC with laboratory terminologies

OBJECTIVE To address the problem of mapping local laboratory terminologies to Logical Observation Identifiers Names and Codes (LOINC). To study different ontology matching algorithms and investigate how the probability of term combinations in LOINC helps to increase match quality and reduce manual effort. MATERIALS AND METHODS We proposed two matching strategies: full name and multi-part. The multi-part approach also considers the occurrence probability of combined concept parts. It can further recommend possible combinations of concept parts to allow more local terms to be mapped. Three real-world laboratory databases from Taiwanese hospitals were used to validate the proposed strategies with respect to different quality measures and execution run time. A comparison with the commonly used tool, Regenstrief LOINC Mapping Assistant (RELMA) Lab Auto Mapper (LAM), was also carried out. RESULTS The new multi-part strategy yields the best match quality, with F-measure values between 89% and 96%. It can automatically match 70-85% of the laboratory terminologies to LOINC. The recommendation step can further propose mapping to (proposed) LOINC concepts for 9-20% of the local terminology concepts. On average, 91% of the local terminology concepts can be correctly mapped to existing or newly proposed LOINC concepts. CONCLUSIONS The mapping quality of the multi-part strategy is significantly better than that of LAM. It enables domain experts to perform LOINC matching with little manual work. The probability of term combinations proved to be a valuable strategy for increasing the quality of match results, providing recommendations for proposed LOINC conepts, and decreasing the run time for match processing.

Annotating Medical Forms Using UMLS

Medical forms are frequently used to document patient data or to collect relevant data for clinical trials. It is crucial to harmonize medical forms in order to improve interoperability and data integration between medical applications. Here we propose a (semi-) automatic annotation of medical forms with concepts of the Unified Medical Language System (UMLS). Our annotation workflow encompasses a novel semantic blocking, sophisticated match techniques and post-processing steps to select reasonable annotations. We evaluate our methods based on reference mappings between medical forms and UMLS, and further manually validate the recommended annotations.

Region Evolution eXplorer – A tool for discovering evolution trends in ontology regions

BackgroundA large number of life science ontologies has been developed to support different application scenarios such as gene annotation or functional analysis. The continuous accumulation of new insights and knowledge affects specific portions in ontologies and thus leads to their adaptation. Therefore, it is valuable to study which ontology parts have been extensively modified or remained unchanged. Users can monitor the evolution of an ontology to improve its further development or apply the knowledge in their applications.ResultsHere we present REX (Region Evolution eXplorer) a web-based system for exploring the evolution of ontology parts (regions). REX provides an analysis platform for currently about 1,000 versions of 16 well-known life science ontologies. Interactive workflows allow an explorative analysis of changing ontology regions and can be used to study evolution trends for long-term periods.ConclusionREX is a web application providing an interactive and user-friendly interface to identify (un)stable regions in large life science ontologies. It is available at http://www.izbi.de/rex.

Evaluating and Improving Annotation Tools for Medical Forms

The annotation of entities with concepts from standardized terminologies and ontologies is of high importance in the life sciences to enhance semantic interoperability, information retrieval and meta-analysis. Unfortunately, medical documents such as clinical forms or electronic health records are still rarely annotated despite the availability of some tools to automatically determine possible annotations. In this study, we comparatively evaluate the quality of two such tools, cTAKES and MetaMap, as well as of a recently proposed annotation approach from our group for annotating medical forms. We also investigate how to improve the match quality of the tools by post-filtering computed annotations as well as by combining several annotation approaches.

A Reuse-Based Annotation Approach for Medical Documents

Annotations are useful to semantically enrich documents and other datasets with concepts of standardized vocabularies and ontologies. In the medical domain, many documents are not annotated at all and manual annotation is a difficult process making automatic annotation methods highly desirable to support human annotators. We propose a reuse-based annotation approach that utilizes previous annotations to annotate similar medical documents. The approach clusters items in documents such as medical forms according to previous ontology-based annotations and uses these clusters to determine candidate annotations for new items. The final annotations are selected according to a new context-based strategy that considers the co-occurrence and semantic relatedness of annotating concepts. The evaluation based on previous UMLS annotations of medical forms shows that the new approaches outperform a baseline approach as well as the use of the MetaMap tool for finding UMLS concepts in medical documents.