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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    Lu Q, Li X, Ghanem M, Guo Y, Pan Het al., 2006,

    Integrating R into Discovery Net

    Richards M, Ghanem M, Osmond M, Guo Y, Hassard Jet al., 2006,

    Grid-based analysis of air pollution data

    , 4th International Workshop on Environmental Applications of Machine Learning (EAML), Publisher: ELSEVIER SCIENCE BV, Pages: 274-286, ISSN: 0304-3800
    Syed J, Ghanem M, Guo Y, 2006,

    Discovery Processes in e-Science: The Discovery Net Approach

    , Concurrency and Computation Practice and Experience, Vol: 19
    Wendel P, Fung A, Ghanem M, Guo Yet al., 2006,

    Designing a Java-based Grid scheduler using commodity services

    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
    Curcin V, Ghanem M, Guo Y, 2005,

    Web services in the life sciences

    , DRUG DISCOVERY TODAY, Vol: 10, Pages: 865-871, ISSN: 1359-6446
    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
    Ghanem M, Chortaras A, Guo Y, Rowe A, Ratcliffe Jet al., 2005,

    A Grid Infrastructure for Mixed Bioinformatics Data and Text Mining

    , 3rd IEEE/ACS International Conference on Computer Systems and Applications (AICCSA-05), Publisher: IEEE, ISSN: 2161-5322
    Gilardoni F, Curcin V, Karunanayake K, Norgaard J, Guo Yet al., 2005,

    Integrated informatics in life and materials sciences: An oxymoron?

    , QSAR and Combinatorial Science, Vol: 24, Pages: 120-130, ISSN: 1611-020X

    The pharmaceutical and chemical industries are facing significant internal and external pressure to boost the experimental efficiency and effectiveness by cutting the direct research costs and reducing the time to market for new sustainable products. Other key issues to secure a competitive return on investment are to enable the rescue of stalled product development projects, abort failing projects early, enhance collaborative multidisciplinary ventures, and ensure an effectual risk management and cost savings through safety testing and failure analysis. Typically, modern organizations rely on a variety of informatics solutions from well-established software vendors. These typically operate on different platforms and are controlled by different management systems using different data types and proprietary formats. Maintaining, integrating, updating and monitoring these powerful, but disparate, ensembles of tools, are an intricate and expensive operation. We present the concept of the Enterprise Discovery Planning (EDP) software platform design to facilitate the optimization of discovery activities at an enterprise level. We also briefly present how Inforsense addresses these issues. © 2005 WILEY-VCH Verlag GmbH & Co. KGaA.

    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
    Kitney R, Freemont P, Guo Y, Hassard Jet al., 2005,

    The biological continuum.

    , Laboratory News, Pages: 22-23
    Osmond M, Guo Y, 2005,

    Adopting and extending portlet technologies for e-Science workflow deployment

    , Publisher: Citeseer
    Tamaddoni-Nezhad A, Chaleil R, Kakas A, Muggleton Set al., 2005,

    Abduction and induction for learning models of inhibition in metabolic networks

    , Los Alamitos, 4th international conference on machine learning and applications, 15 - 17 December 2005, Los Angeles, CA, Publisher: Ieee Computer Soc, Pages: 233-238
    Tan S, Cheng X, Wang B, Xu H, Ghanem MM, Guo Yet al., 2005,

    Using DragPushing to refine centroid text classifiers

    , Pages: 653-654

    We present a novel algorithm, DragPushing, for automatic text classification. Using a training data set, the algorithm first calculates the prototype vectors, or centroids, for each of the available document classes. Using misclassified examples, it then iteratively refines these centroids; by dragging the centroid of a correct class towards a misclassified example and in the same time pushing the centroid of an incorrect class away from the misclassified example. The algorithm is simple to implement and is computationally very efficient. Evaluation experiments conducted on two benchmark collections show that its classification accuracy is comparable to that of more complex methods, such as support vector machines (SVM). © 2005 ACM.

    Watson C, Guo Y, Sheldon J, 2005,

    The impact of workflow technology on drug discovery

    , Drug Discovery Today, Vol: 10, Pages: 1211-1212

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