The Unified Medical Language System (http:// umlsks.nlm.nih.gov) is a repository of biomedical vocabularies developed by the US National Library of Medicine. The UMLS integrates over 2 million names for some 900 000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million relations among these concepts. Vocabularies integrated in the UMLS Metathesaurus include the NCBI taxonomy, Gene Ontology, the Medical Subject Headings (MeSH), OMIM and the Digital Anatomist Symbolic Knowledge Base. UMLS concepts are not only inter-related, but may also be linked to external resources such as GenBank. In addition to data, the UMLS includes tools for customizing the Metathesaurus (MetamorphoSys), for generating lexical variants of concept names (lvg) and for extracting UMLS concepts from text (MetaMap). The UMLS knowledge sources are updated quarterly. All vocabularies are available at no fee for research purposes within an institution, but UMLS users are required to sign a license agreement. The UMLS knowledge sources are distributed on CD-ROM and by FTP.

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The Unified Medical Language System (UMLS): integrating biomedical terminology

This introduction presents the main motivations for the development of Description Logics (DLs) as a formalism for representing knowledge, as well as some important basic notions underlying all systems that have been created in the DL tradition. In addition, we provide the reader with an overview of the entire book and some guidelines for reading it. We first address the relationship between Description Logics and earlier semantic network and frame systems, which represent the original heritage of the field. We delve into some of the key problems encountered with the older efforts. Subsequently, we introduce the basic features of DL languages and related reasoning techniques. DL languages are then viewed as the core of knowledge representation systems, considering both the structure of a DL knowledge base and its associated reasoning services. The development of some implemented knowledge representation systems based on Description Logics and the first applications built with such systems are then reviewed. Finally, we address the relationship of Description Logics to other fields of Computer Science.We also discuss some extensions of the basic representation language machinery; these include features proposed for incorporation in the formalism that originally arose in implemented systems, and features proposed to cope with the needs of certain application domains.

https://assets.cambridge.org/97805218/76254/frontmatter/9780521876254_frontmatter.pdf

The Description Logic Handbook

We review the use on ontologies for the integration of heterogeneous information sources. Based on an in-depth evaluation of existing approaches to this problem we discuss how ontologies are used to support the integration task. We evaluate and compare the languages used to represent the ontologies and the use of mappings between ontologies as well as to connect ontologies with information sources. We also ask for ontology engineering methods and tools used to develop ontologies for information integration. Based on the results of our analysis we summarize the state of the art in ontology-based information integration and name areas of further research activities.

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Ontology-Based Integration of Information — A Survey of Existing Approaches

http://mashup.pubs.dbs.uni-leipzig.de/files/Belleau2008BioRDFTowardsamashuptobuildbioinformaticsknowledge.pdf

Bio2RDF: Towards a mashup to build bioinformatics knowledge systems

Many metabolomics, and other high-content or high-throughput, experiments are set up such that the primary aim is the discovery of biomarker metabolites that can discriminate, with a certain level of certainty, between nominally matched ‘case’ and ‘control’ samples. However, it is unfortunately very easy to find markers that are apparently persuasive but that are in fact entirely spurious, and there are well-known examples in the proteomics literature. The main types of danger are not entirely independent of each other, but include bias, inadequate sample size (especially relative to the number of metabolite variables and to the required statistical power to prove that a biomarker is discriminant), excessive false discovery rate due to multiple hypothesis testing, inappropriate choice of particular numerical methods, and overfitting (generally caused by the failure to perform adequate validation and cross-validation). Many studies fail to take these into account, and thereby fail to discover anything of true significance (despite their claims). We summarise these problems, and provide pointers to a substantial existing literature that should assist in the improved design and evaluation of metabolomics experiments, thereby allowing robust scientific conclusions to be drawn from the available data. We provide a list of some of the simpler checks that might improve one’s confidence that a candidate biomarker is not simply a statistical artefact, and suggest a series of preferred tests and visualisation tools that can assist readers and authors in assessing papers. These tools can be applied to individual metabolites by using multiple univariate tests performed in parallel across all metabolite peaks. They may also be applied to the validation of multivariate models. We stress in particular that classical p-values such as “p < 0.05”, that are often used in biomedicine, are far too optimistic when multiple tests are done simultaneously (as in metabolomics). Ultimately it is desirable that all data and metadata are available electronically, as this allows the entire community to assess conclusions drawn from them. These analyses apply to all high-dimensional ‘omics’ datasets.

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Statistical strategies for avoiding false discoveries in metabolomics and related experiments

The TAMBIS project aims to provide transparent access to disparate biological databases and analysis tools, enabling users to utilize a wide range of resources with the minimum of effort. A prototype system has been developed that includes a knowledge base of biological terminology (the biological Concept Model), a model of the underlying data sources (the Source Model) and a 'knowledge-driven' user interface. Biological concepts are captured in the knowledge base using a description logic called GRAIL. The Concept Model provides the user with the concepts necessary to construct a wide range of multiple-source queries, and the user interface provides a flexible means of constructing and manipulating those queries. The Source Model provides a description of the underlying sources and mappings between terms used in the sources and terms in the biological Concept Model. The Concept Model and Source Model provide a level of indirection that shields the user from source details, providing a high level of source transparency. Source independent, declarative queries formed from terms in the Concept Model are transformed into a set of source dependent, executable procedures. Query formulation, translation and execution is demonstrated using a working example.

TAMBIS: Transparent Access to Multiple Bioinformatics Information Sources

This paper describes the Transparent Access to Multiple Bioinformatics Information Sources project, known as TAMBIS, in which a domain ontology for molecular biology and bioinformatics is used in a retrieval-based information integration system for biologists. The ontology, represented using a description logic and managed by a terminology server, is used both to drive a visual query interface and as a global schema against which complex intersource queries are expressed. These source-independent declarative queries are then rewritten into collections of ordered source-dependent queries for execution by a middleware layer. In bioinformatics, the majority of data sources are not databases but tools with limited accessible interfaces. The ontology helps manage the interoperation between these resources. The paper emphasizes the central role that is played by the ontology in the system. The project distinguishes itself from others in the following ways: the ontology, developed by a biologist, is substantial; the retrieval interface is sophisticated; the description logic is managed by a sophisticated terminology server. A full pilot application is available as a JavaTM applet integrating five sources concerned with proteins. This pilot is currently undergoing field trials with working biologists and is being used to answer real questions in biology, one of which is used as a case study throughout the paper.

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Transparent access to multiple bioinformatics information sources

Motivation: An ontology of biological terminology provides a model of biological concepts that can be used to form a semantic framework for many data storage, retrieval and analysis tasks. Such a semantic framework could be used to underpin a range of important bioinformatics tasks, such as the querying of heterogeneous bioinformatics sources or the systematic annotation of experimental results. Results: This paper provides an overview of an ontology [the Transparent Access to Multiple Biological Information Sources (TAMBIS) ontology or TaO] that describes a wide range of bioinformatics concepts. The present paper describes the mechanisms used for delivering the ontology and discusses the ontology's design and organization, which are crucial for maintaining the coherence of a large collection of concepts and their relationships. Availability: The TAMBIS system, which uses a subset of the TaO described here, is accessible over the Web via http://img.cs.man.ac.uk/tambis (although in the first instance, we will use a password mechanism to limit the load on our server). The complete model is also available on the Web at the above URL. Contact: tambis@cs.man.ac.uk.

An ontology for bioinformatics applications.

Motivation: This paper reports on a survey of bioinformatics tasks currently undertaken by working biologists. The aim was to find the range of tasks that need to be supported and the components needed to do this in a general query system. This enabled a set of evaluation criteria to be used to assess both the biology and mechanical nature of general query systems. Results: A classification of the biological content of the tasks gathered offers a checklist for those tasks (and their specialisations) that should be offered in a general bioinformatics query system. This semantic analysis was contrasted with a syntactic analysis that revealed the small number of components required to describe all bioinformatics questions. Both the range of biological tasks and syntactic task components can be seen to provide a set of bioinformatics requirements for general query systems. These requirements were used to evaluate two bioinformatics query systems.

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A classification of tasks in bioinformatics

Conducting bioinformatic analyses involves biologists in expressing requests over a range of highly heterogeneous information sources and software tools. Such activities are laborious, and require detailed knowledge of the data structures and call interfaces of the different sources. The TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) project seeks to make the diversity in data structures, call interfaces and locations of bioinformatics sources transparent to users. In TAMBIS, queries are expressed in terms of an ontology implemented using a description logic, and queries over the ontology are rewritten to a middleware level for execution over the diverse sources. The paper describes query processing in TAMBIS, focusing in particular on the way source-independent concepts in the ontology are related to source-dependent middleware calls, and describing how the planner identifies efficient ways of evaluating user queries.

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Patricia G. Baker

Papers

TAMBIS: Transparent Access to Multiple Bioinformatics Information Sources

Transparent access to multiple bioinformatics information sources

An ontology for bioinformatics applications.

A classification of tasks in bioinformatics

Query processing in the TAMBIS bioinformatics source integration system