Biomedical subjects

Albert Burger

Publications and source records attributed to Albert Burger.

8 recordsLinked to original sources

PubMed2006

Sealife: a semantic grid browser for the life sciences applied to the study of infectious diseases.

The objective of Sealife is the conception and realisation of a semantic Grid browser for the life sciences, which will link the existing Web to the currently emerging eScience infrastructure. The SeaLife Browser will allow users to automatically link a host of Web servers and Web/Grid services to the Web content he/she is visiting. This will be accomplished using eScience's growing number of Web/Grid Services and its XML-based standards and ontologies. The browser will identify terms in the pages being browsed through the background knowledge held in ontologies. Through the use of Semantic Hyperlinks, which link identified ontology terms to servers and services, the SeaLife Browser will offer a new dimension of context-based information integration. In this paper, we give an overview over the different components of the browser and their interplay. This SeaLife Browser will be demonstrated within three application scenarios in evidence-based medicine, literature & patent mining, and molecular biology, all relating to the study of infectious diseases. The three applications vertically integrate the molecule/cell, the tissue/organ and the patient/population level by covering the analysis of high-throughput screening data for endocytosis (the molecular entry pathway into the cell), the expression of proteins in the spatial context of tissue and organs, and a high-level library on infectious diseases designed for clinicians and their patients. For more information see http://www.biote.ctu-dresden.de/sealife.

Biological Science Disciplines↗

PubMed2005

A criticality-based framework for task composition in multi-agent bioinformatics integration systems.

MOTIVATION: During task composition, such as can be found in distributed query processing, workflow systems and AI planning, decisions have to be made by the system and possibly by users with respect to how a given problem should be solved. Although there is often more than one correct way of solving a given problem, these multiple solutions do not necessarily lead to the same result. Some researchers are addressing this problem by providing data provenance information. Others use expert advice encoded in a supporting knowledge-base. In this paper, we propose an approach that assesses the importance of such decisions with respect to the overall result. We present a way of measuring decision criticality and describe its potential use. RESULTS: A multi-agent bioinformatics integration system is used as the basis of a framework that facilitates such functionality. We propose an agent architecture, and a concrete bioinformatics example (prototype) is used to show how certain decisions may not be critical in the context of more complex tasks.

Algorithms↗

PubMed2005

Providing visualisation support for the analysis of anatomy ontology data.

BACKGROUND: Improvements in technology have been accompanied by the generation of large amounts of complex data. This same technology must be harnessed effectively if the knowledge stored within the data is to be retrieved. Storing data in ontologies aids its management; ontologies serve as controlled vocabularies that promote data exchange and re-use, improving analysis. The Edinburgh Mouse Atlas Project stores the developmental stages of the mouse embryo in anatomy ontologies. This project is looking at the use of visual data overviews for intuitive analysis of the ontology data. RESULTS: A prototype has been developed that visualises the ontologies using directed acyclic graphs in two dimensions, with the ability to study detail in regions of interest in isolation or within the context of the overview. This is followed by the development of a technique that layers individual anatomy ontologies in three-dimensional space, so that relationships across multiple data sets may be mapped using physical links drawn along the third axis. CONCLUSION: Usability evaluations of the applications confirmed advantages in visual analysis of complex data. This project will look next at data input from multiple sources, and continue to develop the techniques presented to provide intuitive identification of relationships that span multiple ontologies.

Anatomy↗

PubMed2005

Anatomical ontologies: names and places in biology.

Ontology has long been the preserve of philosophers and logicians. Recently, ideas from this field have been picked up by computer scientists as a basis for encoding knowledge and with the hope of achieving interoperability and intelligent system behavior. In bioinformatics, ontologies might allow hitherto impossible query and data-mining activities. We review the use of anatomy ontologies to represent space in biological organisms, specifically mouse and human.

Anatomy↗

PubMed2004

Integrating partonomic hierarchies in anatomy ontologies.

BACKGROUND: Anatomy ontologies play an increasingly important role in developing integrated bioinformatics applications. One of the primary relationships between anatomical tissues represented in such ontologies is part-of. As there are a number of ways to divide up the anatomical structure of an organism, each may be represented by more than one valid partonomic (part-of) hierarchy. This raises the issue of how to represent and integrate multiple such hierarchies. RESULTS: In this paper we describe a solution that is based on our work on an anatomy ontology for mouse embryo development, which is part of the Edinburgh Mouse Atlas Project (EMAP). The paper describes the basic conceptual aspects of our approach and discusses strengths and limitations of the proposed solution. A prototype was implemented in Prolog for evaluation purposes. CONCLUSION: With the proposed name set approach, rather than having to standardise hierarchies, it is sufficient to agree on a suitable set of basic tissue terms and their meaning in order to facilitate the integration of multiple partonomic hierarchies.

Anatomy↗

PubMed2004

Formalization of mouse embryo anatomy.

MOTIVATION: The Edinburgh Mouse Atlas and Gene Expression Database project has developed a digital atlas of mouse development to provide a spatio-temporal framework for spatially mapped data such as in situ gene expression and cell lineage. As part of this database, a mouse embryo anatomy ontology has been created. A formalization of this anatomy is required to document its precise semantics and how it is used in the context of the Mouse Atlas. RESULTS: The paper describes the existing anatomy ontology and formalizes aspects of it using a predicate logic based approach. It therefore provides a guide for users of the current version of the ontology, as well as the basis for a description of the anatomy using an ontology language, such as OWL, thus enabling future work on reasoning about the Mouse Atlas in the context of an intelligent gene expression bioinformatics workflow system. The logic has been implemented in a Prolog prototype. AVAILABILITY: The Mouse Atlas is available on-line at http://genex.hgu.mrc.ac.uk

Algorithms↗

PubMed2004

A scalable mediator approach to process large biomedical 3-D images.

The Edinburgh Mouse Atlas is a spatial-temporal framework to store and analyze biological data including three-dimensional (3-D) images that relate to mouse embryo development. The purpose of the system is the analysis and querying of complex spatial patterns, in particular the patterns of gene activity during embryo development. The framework holds large 3-D gray level images and is implemented in part as an object-oriented database. In this paper, we propose a dynamic layered architecture, based on the mediator approach, for the design of a transparent and scalable distributed system which can process objects that can exceed 1 GB in size. The system's data are distributed and/or declustered across a number of image servers and are processed by specialized mediators.

Algorithms↗

PubMed2003

EMAP and EMAGE: a framework for understanding spatially organized data.

The Edinburgh MouseAtlas Project (EMAP) is a time-series of mouse-embryo volumetric models. The models provide a context-free spatial framework onto which structural interpretations and experimental data can be mapped. This enables collation, comparison, and query of complex spatial patterns with respect to each other and with respect to known or hypothesized structure. The atlas also includes a time-dependent anatomical ontology and mapping between the ontology and the spatial models in the form of delineated anatomical regions or tissues. The models provide a natural, graphical context for browsing and visualizing complex data. The Edinburgh Mouse Atlas Gene-Expression Database (EMAGE) is one of the first applications of the EMAP framework and provides a spatially mapped gene-expression database with associated tools for data mapping, submission, and query. In this article, we describe the underlying principles of the Atlas and the gene-expression database, and provide a practical introduction to the use of the EMAP and EMAGE tools, including use of new techniques for whole body gene-expression data capture and mapping.

Animals↗