Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

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  • Conference paper
    Wendel P, Fung A, Ghanem M, Guo Yet al., 2006,

    Designing a Java-based Grid scheduler using commodity services

  • Conference paper
    Curcin V, Ghanem M, Guo YK, Stathis K, Toni F, Curcin V, Ghanem M, Guo YK, Stathis K, Toni Fet al., 2006,

    Building next generation Service-Oriented Architectures using argumentation agents

    , 3rd International Conference on Grid Services Engineering and Management (GSEM 2006), Publisher: Springer Verlag
  • Conference paper
    Lu Q, Li X, Ghanem M, Guo Y, Pan Het al., 2006,

    Integrating R into Discovery Net

  • 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
  • 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

  • Journal article
    Syed J, Ghanem M, Guo Y, 2006,

    Discovery Processes in e-Science: The Discovery Net Approach

    , Concurrency and Computation Practice and Experience, Vol: 19
  • Conference paper
    Davis N, Harkema H, Gaizauskas R, Guo Y, Ghanem M, Barnwell T, Guo YK, Ratcliffe Jet al., 2006,

    Three Approaches to GO-Tagging Biomedical Abstracts

    , SMBM 2006: Second International Symposium on Semantic Mining in Biomedicine., ISSN: 1613-0073
  • Conference paper
    Richards M, Ghanem M, Osmond MA, Guo YK, Hassard J, Ghanem M, Guo Y, Hassard J, Osmond M, Richards Met al., 2006,

    Grid based analysis of air pollution data

    , Proceedings of the 4th European conference on ecological modelling, 4th international workshop on environmental applications of data mining (ECEM/EAML 2004), Publisher: Elsevier, Pages: 274-286, ISSN: 0304-3800
  • Journal article
    Syed J, Ghanem M, Guo YK, 2006,

    Supporting scientific discovery processes in Discovery Net

    , Concurrency and Computation: Practice and Experience, ISSN: 1532-0626
  • Conference paper
    Cohen J, James C, Rahman S, Curcin V B B, Guo Y, Darlington Jet al., 2006,

    Modelling Rail Passenger Movements through e-Science Methods

  • Conference paper
    Liu JG, Ghanem M, Curcin V, Haselwimmer C, Guo Y, Morgan G, Mish Ket al., 2006,

    Distributed, high-performance earthquake deformation analysis and modelling facilitated by Discovery Net

  • Conference paper
    Guo YK, Liu J, Ghanem M, Mish K, Curcin V, Haselwimmer C, Sotiriou D, Muraleetharan K, Taylor L, Guo YK, Liu J, Ghanem M, Mish K, Curcin V, Haselwimmer C, Sotiriou D, Muraleetharan K, Taylor Let al., 2005,

    Bridging the Macro and Micro: a computing intensive earthquake study using Discovery Net

    , SC '05. Conference on High Performance Networking and Computing, Publisher: IEEE Computer Society Press, Pages: 68-68
  • Conference paper
    Wendel P, Ghanem M, Guo YK, 2005,

    Scalable clustering on the Data Grid

    , 4th UK e-Science All Hands Meeting 2005
  • Conference paper
    Ghanem M, Ratcliffe J, Curcin V, Li X, Tattoud R, Scott J, Guo YK, Ghanem M, Ratcliffe J, Curcin V, Li X, Tattoud R, Scott J, Guo YK, Ghanem M, Ratcliffe J, Curcin V, Li X, Tattoud R, Scott J, Guo YK, Ghanem M, Ratcliffe J, Curcin V, Li X, Tattoud R, Scott J, Guo YKet al., 2005,

    Using Text Mining for Understanding Insulin Signalling

    , 4th UK e-Science All Hands Meeting 2005\r\n
  • Conference paper
    Cheng XC, Xu H, Tan S, Wang B, Ghanem M, Guo Yet al., 2005,

    Using Dragpushing as a Refinement Strategy for Text Classifiers

    , Salvador, Brazil

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