Kenneth McLeod

Towards the use of argumentation in bioinformatics

Motivation: Due to different experimental setups and various interpretations of results, the data contained in online bioinformatics resources can be inconsistent, therefore, making it more difficult for users of these resources to assess the suitability and correctness of the answers to their queries. This work investigates the role of argumentation systems to help users evaluate such answers. More specifically, it looks closely at a gene expression case study, creating an appropriate representation of the underlying data and series of rules that are used by a third-party argumentation engine to reason over the query results provided by the mouse gene expression database EMAGE. Results: A prototype using the ASPIC argumentation engine has been implemented and a preliminary evaluation carried out. This evaluation suggested that argumentation can be used to deal with inconsistent data in biological resources. Availability: The ASPIC argumentation engine is available from http://www.argumentation.org. EMAGE gene expression data can be obtained from http://genex.hgu.mrc.ac.uk. The argumentation rules for the gene expression example are available from the lead author upon request. Contact: kcm1@hw.ac.uk

A conceptual approach to gene expression analysis enhanced by visual analytics

The analysis of gene expression data is a complex task for biologists wishing to understand the role of genes in the formation of diseases such as cancer. Biologists need greater support when trying to discover, and comprehend, new relationships within their data. In this paper, we describe an approach to the analysis of gene expression data where overlapping groupings are generated by Formal Concept Analysis and interactively analyzed in a tool called CUBIST. The CUBIST workflow involves querying a semantic database and converting the result into a formal context, which can be simplified to make it manageable, before it is visualized as a concept lattice and associated charts.

Knowledge-driven enhancements for task composition in bioinformatics

BackgroundA key application area of semantic technologies is the fast-developing field of bioinformatics. Sealife was a project within this field with the aim of creating semantics-based web browsing capabilities for the Life Sciences. This includes meaningfully linking significant terms from the text of a web page to executable web services. It also involves the semantic mark-up of biological terms, linking them to biomedical ontologies, then discovering and executing services based on terms that interest the user.ResultsA system was produced which allows a user to identify terms of interest on a web page and subsequently connects these to a choice of web services which can make use of these inputs. Elements of Artificial Intelligence Planning build on this to present a choice of higher level goals, which can then be broken down to construct a workflow. An Argumentation System was implemented to evaluate the results produced by three different gene expression databases. An evaluation of these modules was carried out on users from a variety of backgrounds. Users with little knowledge of web services were able to achieve tasks that used several services in much less time than they would have taken to do this manually. The Argumentation System was also considered a useful resource and feedback was collected on the best way to present results.ConclusionOverall the system represents a move forward in helping users to both construct workflows and analyse results by incorporating specific domain knowledge into the software. It also provides a mechanism by which web pages can be linked to web services. However, this work covers a specific domain and much co-ordinated effort is needed to make all web services available for use in such a way, i.e. the integration of underlying knowledge is a difficult but essential task.

PhenoImageShare: An image annotation and query infrastructure

BackgroundHigh throughput imaging is now available to many groups and it is possible to generate a large quantity of high quality images quickly. Managing this data, consistently annotating it, or making it available to the community are all challenges that come with these methods.ResultsPhenoImageShare provides an ontology-enabled lightweight image data query, annotation service and a single point of access backed by a Solr server for programmatic access to an integrated image collection enabling improved community access. PhenoImageShare also provides an easy to use online image annotation tool with functionality to draw regions of interest on images and to annotate them with terms from an autosuggest-enabled ontology-lookup widget. The provenance of each image, and annotation, is kept and links to original resources are provided. The semantic and intuitive search interface is species and imaging technology neutral. PhenoImageShare now provides access to annotation for over 100,000 images for 2 species.ConclusionThe PhenoImageShare platform provides underlying infrastructure for both programmatic access and user-facing tools for biologists enabling the query and annotation of federated images. PhenoImageShare is accessible online at http://www.phenoimageshare.org.

Towards the Semantic Representation of Biological Images: From Pixels to Regions

Biomedical images and models contain vast amounts of information. Regrettably, much of this information is only accessible by domain experts. This paper describes a biological use case in which this situation occurs. Motivation is given for describing images, from this use case, semantically. Furthermore, links are provided to the medical domain, demonstrating the transferability of this work. Subsequently, it is shown that a semantic representation in which every pixel is featured is needlessly expensive. This motivates the discussion of more abstract renditions, which are dealt with next. As part of this, the paper discusses the suitability of existing technologies. In particular, Region Connection Calculus and one implementation of the W3C Geospatial Vocabulary are considered. It transpires that the abstract representations provide a basic description that enables the user to perform a subset of the desired queries. However, a more complex depiction is required for this use case.

Argudas: lessons for argumentation in biology based on a gene expression use case

BackgroundIn situ hybridisation gene expression information helps biologists identify where a gene is expressed. However, the databases that republish the experimental information online are often both incomplete and inconsistent. Non-monotonic reasoning can help resolve such difficulties - one such form of reasoning is computational argumentation. Essentially this involves asking a computer to debate (i.e. reason about) the validity of a particular statement. Arguments are produced for both sides - the statement is true and, the statement is false - then the most powerful argument is used. In this work the computer is asked to debate whether or not a gene is expressed in a particular mouse anatomical structure. The information generated during the debate can be passed to the biological end-user, enabling their own decision-making process.ResultsThis paper examines the evolution of a system, Argudas, which tests using computational argumentation in an in situ gene hybridisation gene expression use case. Argudas reasons using information extracted from several different online resources that publish gene expression information for the mouse. The development and evaluation of two prototypes is discussed. Throughout a number of issues shall be raised including the appropriateness of computational argumentation in biology and the challenges faced when integrating apparently similar online biological databases.ConclusionsFrom the work described in this paper it is clear that for argumentation to be effective in the biological domain the argumentation community need to develop further the tools and resources they provide. Additionally, the biological community must tackle the incongruity between overlapping and adjacent resources, thus facilitating the integration and modelling of biological information. Finally, this work highlights both the importance of, and difficulty in creating, a good model of the domain.

A visual analytics technique for exploring gene expression in the developing mouse embryo

This paper describes a novel visual analytics technique for exploring gene expression in the developing mouse embryo. The majority of existing techniques either visualise a single gene profile or a single tissue profile, whereas the technique presented here combines both - visualising the genes expressed in each tissue in a group of tissues (the components of the developing heart, for example). The technique is presented using data, provided by the Edinburgh Mouse Atlas Project, of gene expression assays conducted on tissues of the developing mouse embryo and a corresponding hierarchical graph of tissues defining the mouse anatomy. By specifying a particular tissue, such as the heart, and a particular stage of development, a Formal Context is computed making use of the hierarchical mouse anatomy so that the resulting Formal Concept Lattice visualises the components of the specified tissue and the genes expressed in each component. An algorithm is presented that defines the computation the Formal Context. Examples of resulting lattices are given to illustrate the technique and show how it can provide useful information to researchers of gene expression and embryo development.

The ‘straight mouse’: defining anatomical axes in 3D embryo models

Abstract A primary objective of the eMouseAtlas Project is to enable 3D spatial mapping of whole embryo gene expression data to capture complex 3D patterns for indexing, visualization, cross-comparison and analysis. For this we have developed a spatio-temporal framework based on 3D models of embryos at different stages of development coupled with an anatomical ontology. Here we introduce a method of defining coordinate axes that correspond to the anatomical or biologically relevant anterior–posterior (A–P), dorsal–ventral (D–V) and left–right (L–R) directions. These enable more sophisticated query and analysis of the data with biologically relevant associations, and provide novel data visualizations that can reveal patterns that are otherwise difficult to detect in the standard 3D coordinate space. These anatomical coordinates are defined using the concept of a ‘straight mouse-embryo’ within which the anatomical coordinates are Cartesian. The straight embryo model has been mapped via a complex non-linear transform onto the standard embryo model. We explore the utility of this anatomical coordinate system in elucidating the spatial relationship of spatially mapped embryonic ‘Fibroblast growth factor’ gene expression patterns, and we discuss the importance of this technology in summarizing complex multimodal mouse embryo image data from gene expression and anatomy studies. Database URL: www.emouseatlas.org