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• Conference paper
  Nobari S, Tauheed F, Heinis T, Karras P, Bressan S, Ailamaki Aet al., 2013,

  TOUCH: in-memory spatial join by hierarchical data-oriented partitioning

  , Pages: 701-712
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• Journal article
  Yang X, Han R, Guo Y, Bradley J, Cox B, Dickinson R, Kitney Ret al., 2012,

  Modelling and performance analysis of clinical pathways using the stochastic process algebra PEPA

  , Bmc Bioinformatics, Vol: 13, ISSN: 1471-2105
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• Conference paper
  Holehouse A, Yang X, Adcock I, Guo Yet al., 2012,

  Developing a novel integrated model of p38 MAPK and glucocorticoid signalling pathways

  , Pages: 69-76
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• Journal article
  Huntley DM, Pandis I, Butcher SA, Ackers JPet al., 2010,

  Bioinformatic analysis of <i>Entamoeba histolytica</i> SINE1 elements

  , BMC GENOMICS, Vol: 11, ISSN: 1471-2164
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  • Citations: 11
• Journal article
  Curcin V, Ghanem M, Guo Y, 2010,

  The design and implementation of a workflow analysis tool

  , Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 368, Pages: 4193-4208
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• Journal article
  Curcin V, Ghanem M, Guo Y, 2010,

  Polymorphic type framework for scientific workflows with relational data model

  , International Journal of Business Process Integration and Management, Vol: 5, Pages: 45+-45+, ISSN: 1741-8763
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• Journal article
  Wang FZ, Helian N, Wu S, Guo Y, Deng DY, Meng L, Zhang W, Crowcroft J, Bacon J, Parker MAet al., 2009,

  Eight Times Acceleration of Geospatial Data Archiving and Distribution on the Grids (vol 47, pg 1444, 2009)

  , IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, Vol: 47, Pages: 2988-2988, ISSN: 0196-2892
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• Journal article
  Curcin V, Ghanem M, Guo Y, 2009,

  Analysing scientific workflows with Computational Tree Logic. Journal of Cluster Computing

  , Journal of Cluster Computing: Special Issue of Recent Advances in e-Science, ISSN: 1386-7857

  Motivated by the widespread use of workflow systems in e-Science applications, this article introduces a formal analysis framework for the verification and profiling of the control flow aspects of scientific workflows. The framework relies on process algebras that characterise each workflow component with a process behaviour, which is then used to build a CTL state model that can be reasoned about. We demonstrate the benefits of the approach by modelling the control flow behaviour of the Discovery Net system, one of the earliest workflow-based e-Science systems, and present how some key properties of workflows and individual service utilisation can be queried at design time. Our approach is generic and can be applied easily to modelling workflows developed in any other system. It also provides a formal basis for the comparison of control aspects of e-Science workflow systems and a design method for future systems.

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• Conference paper
  Curcin V, Ghanem M, Guo Y, Darlington Jet al., 2008,

  Mining adverse drug reactions with e-science workflows.

  , Proceedings of the 4th Cairo International Biomedical Engineering Conference, 2008. CIBEC 2008
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• Book chapter
  Ghanem M, Curcin V, Wendel P, Guo Yet al., 2008,

  Building and using analytical workflows in Discovery Net

  , Data Mining Techniques in Grid Environments. Dubitzky, Werner (Ed)., Publisher: Wiley-Blackwell, Pages: 119-140, ISBN: 9780470512586

  The Discovery Net platform is built around a workflow model for integrating distributed data sources and analytical tools. The platform was originally designed to support the design and execution of distributed data mining tasks within a grid-based environment. However, over the years it has evolved into a generic data analysis platform with applications in such diverse areas as bioinformatics, cheminformatics, text mining and business intelligence. In this work we present our experience in designing the platform and map out the evolution paths for a workflow language, and its architecture, that need to address the requirements of different scientific domains.

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• Journal article
  Ma Y, Richards M, Ghanem M, Guo Y, Hassard Jet al., 2008,

  Air pollution monitoring and mining based on sensor grid in London

  , Sensors, Vol: 8, Pages: 3601-3623, ISSN: 1424-8220

  In this paper, we present a distributed infrastructure based on wireless sensors network and Grid computing technology for air pollution monitoring and mining, which aims to develop low-cost and ubiquitous sensor networks to collect real-time, large scale and comprehensive environmental data from road traffic emissions for air pollution monitoring in urban environment. The main informatics challenges in respect to constructing the high-throughput sensor Grid are discussed in this paper. We present a twolayer network framework, a P2P e-Science Grid architecture, and the distributed data mining algorithm as the solutions to address the challenges. We simulated the system in TinyOS to examine the operation of each sensor as well as the networking performance. We also present the distributed data mining result to examine the effectiveness of the algorithm.

  • Abstract
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• Conference paper
  Curcin V, Ghanem M, Wendel P, Guo Yet al., 2007,

  Heterogeneous workflows in scientific workflow systems

  , Publisher: Springer, Pages: 204-211
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• Conference paper
  Liu J, Ghanem M, Curcin V, Haselwimmer C, Guo Y, Morgan G, Mish Ket al., 2006,

  Achievements and Experiences from a Grid-Based Earthquake Analysis and Modelling Study \r\n

  , Publisher: IEEE Computer Society Press

  We have developed and used a grid-based geoinformatics infrastructure and analytical methods for investigating the relationship between macro and microscale earthquake deformational processes by linking geographically distributed and computationally intensive earthquake monitoring and modelling tools. Using this infrastructure, measurement of lateral co-seismic deformation is carried out with imageodesy algorithms running on servers at the London eScience Centre. The resultant deformation field is used to initialise geomechanical simulations of the earthquake deformation running on supercomputers based at the University of Oklahoma. This paper describes the details of our work, summarizes our scientific results and details our experiences from implementing and testing the distributed infrastructure and analysis workflow.

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• Journal article
  Lu Q, Hao P, Curcin V, He W, Li Y-Y, Luo Q-M, Guo Y-K, Li Y-Xet al., 2006,

  KDE bioscience: Platform for bioinformatics analysis workflows

  , JOURNAL OF BIOMEDICAL INFORMATICS, Vol: 39, Pages: 440-450, ISSN: 1532-0464
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  • Citations: 19
• Conference paper
  Kakas A, Tamaddoni Nezhad A, Muggleton S, Chaleil Ret al., 2006,

  Application of abductive ILP to learning metabolic network inhibition from temporal data

  , Publisher: Springer, Pages: 209-230, ISSN: 0885-6125

  In this paper we use a logic-based representation and a combination of Abduction and Induction to model inhibition in metabolic networks. In general, the integration of abduction and induction is required when the following two conditions hold. Firstly, the given background knowledge is incomplete. Secondly, the problem must require the learning\r\nof general rules in the circumstance in which the hypothesis language is disjoint from the observation language. Both these conditions hold in the application considered in this paper. Inhibition is very important from the therapeutic point of view since many substances designed to be used as drugs can have an inhibitory effect on other enzymes. Any system able to predict the inhibitory effect of substances on the metabolic network would therefore be very useful in assessing the potential harmful side-effects of drugs. In modelling the phenomenon\r\nof inhibition in metabolic networks, background knowledge is used which describes the network topology and functional classes of inhibitors and enzymes. This background knowledge, which represents the present state of understanding, is incomplete. In order to overcome this incompleteness hypotheses are considered which consist of a mixture of specific inhibitions of enzymes (ground facts) together with general (non-ground) rules which predict classes of enzymes likely to be inhibited by the toxin. The foreground examples are derived from\r\nin vivo experiments involving NMR analysis of time-varying metabolite concentrations in rat urine following injections of toxins. The modelÆs performance is evaluated on training and test sets randomly generated from a real metabolic network. It is shown that even in\r\nthe case where the hypotheses are restricted to be ground, the predictive accuracy increases with the number of training examples and in all cases exceeds the default (majority class).\r\nExperimental results also suggest that when sufficient training data is provided

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