Prototype Software
New technologies and approaches for standardised high throughput generation, analysis and validation of in situ gene expression patterns in early human development were evaluated by the DGEMap team. For further details of the prototype software and technology that was developed during the project please see the papers listed below.
Wang, X; Baldock, R; and Lindsay, S. 2010 (in press). From spatial-data to 3D models of the developing human brain. Elsevier Methods.
This paper describes software that allows 3D domains to be reconstructed by interpolating between sparse 2D gene expression patterns that have been mapped to 3D representative models of corresponding human developmental stages. A set of procedures are proposed to infer expression domains in these gaps. The procedures, which are connected in a serial way, include components clustering, components track- ing, shape matching and points interpolation. Each procedure consists of a graphical user interface and a set of algorithms. Results on exemplar gene data are provided.
Atkinson, M.P.; van Hemert, J.I; Han, L; Hume, A; and Liew, C.S. 2009 (forthcoming). A distributed architecture for data mining and integration. In DADC '09: Proceedings of the second international workshop on Data-aware distributed computing. New York, NY, USA. pp 11-20.
This paper presents the rationale for a new architecture to support a significant increase in the scale of data integration and data mining. It proposes the composition into one framework of (1) data mining and (2) data access and integration. We name the combined activity DMI. It supports enactment of DMI processes across heterogeneous and distributed data resources and data mining services. It posits that a useful division can be made between the facilities established to support the definition of DMI processes and the computational infrastructure provided to enact DMI processes. Communication between those two divisions is restricted to requests submitted to gateway services in a canonical DMI language. Larger-scale processes are enabled by incremental refinement of DMI-process definitions often by recomposition of lower-level definitions. Autonomous evolution of data resources and services is supported by types and descriptions which will support detection of inconsistencies and semi-automatic insertion of adaptations. These architectural ideas are being evaluated in a feasibility study that involves an application scenario and representatives of the community.
Barker, A. and van Hemert, J.I. 2008. Scientific Workflow: A Survey and Research Directions. In Wyrzykowski, R, editor. Processing and Applied Mathematics. Berlin: Springer-Verlag. pp746-753.
Workflow technologies are emerging as the dominant approach to coordinate groups of distributed services. However with a space filled with competing specifications, standards and frameworks from multiple domains, choosing the right tool for the job is not always a straightforward task. Researchers are often unaware of the range of technology that already exists and focus on implementing yet another proprietary workflow system. As an antidote to this common problem, this paper presents a concise survey of existing workflow technology from the business and scientific domain and makes a number of key suggestions towards the future development of scientific workflow systems.