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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  • 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.

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

    Heterogeneous workflows in scientific workflow systems

    , Publisher: Springer, Pages: 204-211
  • 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.

  • 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

  • Conference paper
    Richards M, Ghanem M, Osmond MA, Guo YK, Hassard Jet 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
  • Conference paper
    El-Shishiny H, Soliman THA, Emam I, 2006,

    Mining drug targets: The challenges and a proposed framework

    , Pages: 239-244, ISSN: 1530-1346

    Drug target identification, being the first phase in drug discovery is becoming an overly time consuming process and in many cases produces inefficient results due to failure of conventional approaches to investigate large scale data. The main goal of this work is to identify drug targets, where there are genes or proteins associated with specific diseases. With the help of Microarray technology, the relationship between biological entities such as protein-protein, gene-gene and related chemical compounds are used as a means to identify drug targets. In this work, we focus on the challenges facing drug target discovery and propose a novel unified framework for mining disease related drug targets. © 2006 IEEE.

  • 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
    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
    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
  • Journal article
    Curcin V, Ghanem M, Guo Y, 2005,

    Web services in the life sciences

    , Drug discovery today, Vol: 10, Pages: 865-871
  • Conference paper
    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
  • Journal article
    Gilardoni F, Curcin V, Karunanayake K, Norgaard J, Guo Yet al., 2005,

    Integrated informatics in life and materials sciences: An oxymoron?

    , QSAR & Combinatorial Science, Vol: 24, Pages: 120-130
  • Conference paper
    Ghanem M, Guo Y, Hassard J, Osmond M, Richards Met al., 2004,

    Sensor Grids for air pollution monitoring

    , 3rd UK e-Science All-hands Conference AHM 2004, Nottingham, UK, Publisher: EPSRC, Pages: 106-113
  • Conference paper
    Au, AKTP, Curcin V, Ghanem M, Giannadakis N, Guo Y, Jafri MA, Osmond M, Oleynikov A, Rowe AS, Syed J, Wendel P, Zhang Yet al., 2004,

    Why grid-based data mining matters? fighting natural disasters on the grid: from SARS to land slides

    , UK e-science all-hands meeting, AHM 2004, Nottingham, UK, September 2004, Publisher: EPSRC, Pages: 121-126

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