Brunel Allan Tucker

Allan Tucker - Research Lecturer

School of Information System Computing and Mathemtics

CIDA Centre for Intelligent Data Analysis

Contact Information
E-Mail : allan.tucker@brunel.ac.uk
Telephone : 44 (0)1895 266933

NEWS: Invited Talk at the Royal Society of Medicine: What is Big Data? How can we analyse it?

Research and Interests

My first degree was in Cognitive Science at Sheffield University where I became interested in models of brain function and human and animal behaviour. I am also interested in learning AI models of multivariate time series in order to try and understand the underlying processes generating such data. My Ph.D at Birkbeck College, entitled "The Automatic Explanation of Multivariate Time Series" was sponsored by the Engineering and Physical Sciences Research Council; Honeywell Hi-Spec Solutions, UK; and Honeywell HTC, USA. I spent two Summers working at Honeywell HTC on research and development.

Currently, I am working as a research lecturer at Brunel University. My projects include extending my work on time series using evolutionary computation and Bayesian networks with various datasets including high dimensional gene expression data, and visual field test data from Moorfield’s Eye Hospital with two PhD students, Stefano Ceccon and Yuanxi Li. Previously, my PhD student, Emma Steele worked in conjunction with Leiden University Medical School on gene regulatory networks and this work is currently being expanded to explore networks across systems of increasing complexity. I have recently started a collaboration exploring the dynamics of fish populations in the Northern Atlantic using Hidden Markov Models in conjunction with the Canadian Department of Fisheries and Oceans. In a slightly different strand of my research, a PhD student, Fadra Hassan is working on pedestrian simulation models using Cellular Automata with evolutionary algorithms to design public spaces.

I have secured grants from

  • NERC
  • National Grid
  • The Royal Society
  • EPSRC
  • DEFRA

    I am or have previously been on the Program committee of

  • Knowledge Discovery in Databases - KDD (Research),
  • AAAI (Computational Sustainability and AI Track),
  • IJCAI (Computational Sustainability and AI Track),
  • AI in medicine Europe,
  • Intelligent Data Analysis Symposia,
  • International Conference of the European Federation for Medical Informatics
  • IDAMAP workshop series.

    I have reviewed for numerous journals including:

  • Nature Protocols,
  • PLOS ONE,
  • IEEE Transactions on Evolutionary Computation,
  • Special Issue of Machine Learning,
  • Journal of Biomedical Engineering,
  • AI in Medicine,
  • Annals of Operational Research,
  • IEEE Transactions on Information Technology in Biomedicine.

  • I was co-chair for IDAMAP 2007 in Amsterdam, IDAMAP 2008 in Washington, US, IDAMAP 2011 in Bled, Slovenia, and for IDA 2012 in Helsinki.
  • I am currently general chair for IDA 2013 to be held in London
  • I recently co-edited a special issue of Methods of Information in Medicine.
  • I am on the board for AI in Medicine and a member of the Intelligent Data Analysis council.
  • I am on the editorial board for the journal of biomedical informatics.


    Invited Talks:

    How to Analyse Big Data, The Royal Society of Medicine, 2014
    Artificial intelligence, usefull tool or robot overlords?, London Skeptics in the Pub, (and various other pubsi n South East England) 2014
    Machine Learning Approaches to Modelling Fisheries Data, Plymouth Marine Laboratory, 2013
    The Intelligent Data Analysis of Natural Process Data, Harbin Institute of Technology, China (also to Makerere University School of Computing and IT, Uganda via Skype) 2012
    Probabilistic Models for Understanding Ecological Data: Case studies in Seeds, Fish and Coral, Keynote Speech at CompSust 2012, Copenhagen
    A Machine Learning Approach to Identifying Functionally Similar Marine Species, University of East Anglia 2012
    Combining heterogeneous data to reverse engineer regulatory networks, Rothamsted Research 2011
    Data Mining, British Computer Society 2011
    Bioinformatics tools in predictive ecology: applications to fisheries, The Royal Society 2011
    From Genes to Populations: The Intelligent Data Analysis of Biological Data, Leeds University 2011
    The Reverse Engineering of Gene Regulatory Networks, Imperial College London 2010
    Machine Learning for predicting fish population interaction, CEFAS, UK 2009.
    Bayesian networks and how they can help us to explore fish species interaction in the Northern gulf of St Lawrence, Maurice-Lamontagne Institute, Mont Joli, Canada 2007
    Making the Most of Small Samples When Classifying High-Dimensional Micro-Array Data, The Institute of Child Health, 2004
    Generating Robust and Consensus Clusters from Gene Expression Data, The European Bioinformatics Institute, 2003
    A Framework for Modelling Short, High-Dimensional Multivariate Time Series: Preliminary Results in Virus Gene Expression Data Analysis, Saint George’s Hospital, 2001
    Bridging The Gap Between Applications and Tools: Modelling Multivariate Time Series, The Royal Statistical Society 1999

    Publications:

    Journal Papers

    Many of these can be downloaded from BURA from my official homepage.

    (In Press) Bo, V. Curtis, T. Lysenko, A. Saqi, M. Swift, S. and Tucker, A. Discovering Study-Specific Gene Regulatory Networks, PLOS ONE.

    (In Press) Hassan, F. Swift, S. Tucker, A. Using Heuristic Search with Pedestrian Simulation Statistics to Find Feasible Spatial Layout Design Elements, Journal of Algorithmic Optimisation.

    (2014) Sacchi, L. Tucker, A. Counsell, S. Swift, S. Improving predictive models of glaucoma severity by incorporating quality indicators, Artificial Intelligence In Medicine.

    (2013) Stefano Ceccon , David Garway-Heath , David Crabb , Allan Tucker, Exploring Early Glaucoma and the Visual Field Test: Classification and Clustering using Bayesian Networks, IEEE Journal of Biomedical and Health Informatics

    (2013) Li, Yuanxi, Swift, Stephen, Tucker, Allan, "Modelling and Analysing the Dynamics of Disease Progression from Cross-Sectional Studies Corresponding Author", Journal of Biomedical Informatics, DOI 10.1016/j.jbi.2012.11.003

    (2012) Paterson, A. Ashtari, M. Ribe, D. Stenbeck, G. Tucker, A. "Intelligent Data Analysis to Model and Understand Live Cell Time-Lapse Sequences", Methods of Information in Medicine 51: 279-367

    (2012) Westgarth-Smith, A.R. Roy, D.B. Scholze, M. Tucker, A. Sumpter, J.P. "The role of the North Atlantic Oscillation in controlling UK butterfly population size and phenology" Ecological Entomology 37 (3) : 221- 232

    (2012) Tucker A. Duplisea D., Bioinformatics tools in predictive ecology: Applications to fisheries, Philosophical Transactions of the Royal Society: Part B 367 (1586) : 279- 290

    (2011) Anvar, S.Y, Tucker, A. Vinciotti, V. Venema, A. van Ommen, G.J.B. van der Maarel, S.M. Raz, V. ‘t Hoen, P.A.C. Interspecies translation of disease networks increases robustness and predictive accuracy, PLOS Computational Biology 7 (11) : e1002258

    (2011) Al-Hamzawi, R. Yu, K. Vinciotti, V. Tucker, A. , Prior elicitation for mixed quantile regression with an allometric model, Environmetrics, DOI: 10.1002/env.1118

    (2010) Sheng, WG., Tucker, A. and Liu, XH., A niching genetic k-means algorithm and its applications to gene expression data, Soft Computing - A Fusion of Foundations, Methodologies and Applications 14 (1) : 9- 19

    (2010) Tucker, A. and Garway-Heath, D., The pseudotemporal bootstrap for predicting glaucoma from cross-sectional visual field data., IEEE Trans Inf Technol Biomed 14 (1) : 79- 85

    (2010) Anvar, SY., 't Hoen, PA. and Tucker, A., The identification of informative genes from multiple datasets with increasing complexity., BMC Bioinformatics 11 32-

    (2009) Steele, E., Tucker, A., 't Hoen, PAC. and Schuemie, MJ., Literature-based priors for gene regulatory networks, Bioinformatics 25 (14) : 1768- 1774

    (2009) Peek, N., Combi, C. and Tucker, A., Biomedical data mining (Editorial), Methods of Information in Medicine 48 (3) : 225- 228

    (2008) Steele, E. and Tucker, A., Consensus and meta-analysis regulatory networks for combining multiple microarray gene expression datasets, Journal of Biomedical Informatics 41 (6) : 914- 926

    (2008) Wang, Z., Yang, F., Ho, DWC., Swift, S., Tucker, A. and Liu, X., Stochastic dynamic modelling of short gene expression time series data, IEEE Transactions on Nanobioscience 7 (1) : 44- 55

    (2006) Vinciotti, V., Tucker, A., Kellam, P. and Liu, X., The robust selection of predictive genes via a simple classifier, Applied Bioinformatics 5 (1) : 1- 11

    (2006) Tucker, A., 't Hoen, PAC., Vinciotti, V. and Liu, X., Temporal Bayesian classifiers for modelling muscular dystrophy expression data, Intelligent Data Analysis 10 (5) : 441- 455

    (2006) Counsell, S., Swift, S., Tucker, A. and Mendes, E., Object-oriented cohesion subjectivity amongst experienced and novice developers: an empirical study, ACM SIGSOFT Software Engineering Notes 31 (5) : 1- 10

    (2006) Strouthidis, NG., Vinciotti, V., Tucker, AJ., Gardiner, SK., Crabb, DP. and Garway-Heath, DF., Structure and function in glaucoma: the relationship between a functional visual field map and an anatomic retinal map, Investigative Opthalmology and Visual Science 47 (12) : 5356- 5362

    (2005) Tucker, A., Crampton, J. and Swift, S., RGFGA: an efficient representation and crossover for grouping genetic algorithms, Evolutionary Computation 13 (4) : 477- 499

    (2005) Tucker, A., Vinciotti, V., Liu, X. and Garway-Heath, D., A spatio-temporal Bayesian network classifier for understanding visual field deterioration, Artificial Intelligence in Medicine 34 (2) : 163- 177

    (2004) Tucker, A. and Liu, X., A bayesian network approach to explaining time series with changing structure, Intelligent Data Analysis 8 (5) : 469- 480

    (2004) Swift, S., Tucker, A. and Liu, X., An analysis of scalable methods for clustering high-dimensional gene expression data, Annals of Mathematics, Computing and Teleinformatics 1 (2) : 80- 89

    (2004) Swift, S., Tucker, A., Vinciotti, V., Martin, N., Orengo, C., Liu, X. and Kellam, P., Consensus clustering and functional interpretation of gene-expression data, Genome Biology 5 (11) : R94- R94

    (2002) Kellam, P., Liu, X., Martin, N., Orenga, C., Swift, S. and Tucker, A., A framework for modelling virus gene expression data, Intelligent Data Analysis 6 (3) : 265- 279

    (2002) Counsell, S., Liu, X., McFall, J., Swift, S. and Tucker, A., Evolutionary algorithms for grouping high dimensional email data, Intelligent Data Analysis 6 (6) : 503- 516

    (2001) Swift, S., Tucker, A., Martin, N. and Liu, X., Grouping multivariate time series variables: applications to chemical process and visual field data, Knowledge-Based Systems 14 (3-4) : 147- 154

    (2001) Tucker, A., Swift, S. and Liu, X., Variable grouping in multivariate time series via correlation, IEEE Transactions on Systems, Man and Cybernetics, Part B 31 (2) : 235- 245

    (2001) Tucker, A., Liu, X. and Ogden-Swift, A., Evolutionary learning of dynamic probabilistic models with large time lags, International Journal of Intelligent Systems 16 621- 645

    Conference Papers

    (2013) Bo, V. Lysenko, A. Saqi, M. Habash, D. and Tucker, A. "Integrating Multiple Studies of Wheat Microarray Data to Identify treatment-Specific Regulatory Networks" Advances in Intelligent Data Analysis XI, LNCS 7619, Springer-Verlag

    (2013) Tucker, A. Hollmen, F. Siebes, A. Swift, S. (ed.s) "Advances in Intelligent Data Analysis XII", LNCS 8207, Springer-Verlag

    (2012) Hollmen, J. Klawonn, F. Tucker, A. (ed.s) "Advances in Intelligent Data Analysis XI", LNCS 7619, Springer-Verlag

    (2011)Hassan, F. Tucker, A. Automatic Layout Design Solution, Proceedings of the Symposium on Intelligent Data Analysis 2011, LNCS 7014, Springer Verlag

    (2011) Ceccon, S. Garway-Heath, D. Crabb, D. and Tucker, A. The Dynamic Stage Bayesian Network: identifying and modelling key stages in a temporal process, Proceedings of the Symposium on Intelligent Data Analysis 2011, LNCS 7014, Springer Verlag

    (2011) Tucker, A. Duplisea, D. Integrating Marine Species Biomass Data by Modelling Functional Knowledge, Proceedings of the Symposium on Intelligent Data Analysis 2011, LNCS 7014, pp 352-363, Springer Verlag

    (2011) Ceccon, S. & Tucker, A. Ensembles of Bayesian network classifiers using glaucoma data and expertise, Supervised and Unsupervised Ensemble Methods and their Applications, Studies in Computational Intelligence, Springer

    (2010) Ceccon, S., Garway-Heath, D., Crabb, D. and Tucker, A., Investigations of clinical metrics and anatomical expertise with Bayesian network models for classification in early glaucoma, Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications (SUEMA 2010), held at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010)

    (2010) Hassan, F. and Tucker, A., Using cellular automata pedestrian flow statistics with heuristic search to automatically design spatial layout, 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2010)

    (2010) Hassan, F. and Tucker, A., Using uniform crossover to refine simulated annealing solutions for automatic design of spatial layouts, International Conference on Evolutionary Computation (ICAC)

    (2010) Li, Y. and Tucker, A., Uncovering disease regions using pseudo time-series trajectories on clinical trial data, 3rd International Conference on BioMedical Engineering and Informatics (BMEI 2010)

    (2010) Tucker, A., Swift, S., Counsell, S., Kent, S., Dickie, J., Liu, K. and Turner, R., Data mining the millennium seedbank at Kew, Workshop on Data Mining in Agriculture (DMA 2010) at the Industrial Conference on Data Mining (ICDM)

    (2009) Steele, E. and Tucker, A., Selecting and weighting data for building consensus gene regulatory networks. In: Adams, NM., Robardet, C., Siebes, A. and Boulicaut, J. eds. Advances in Intelligent Data Analysis VIII. Berlin/Heidelberg : Springer-Verlag (5772/2009) : 190- 201

    (2009) Cain, J., Counsell, S., Swift, S. and Tucker, A., An application of intelligent data analysis techniques to a large software engineering dataset. In: Advances in Intelligent Data Analysis VIII. Berlin/Heidelberg : Springer (5772/2009) : 261- 272

    (2009) Li, X., Garway-Heath, D. and Tucker, A., Using pseudo time-series trajectories to explore disease regions in glaucoma, Fourteenth Workshop on Intelligent Data Analysis in bioMedicine and Pharmacology (IDAMAP 2009)P

    (2007) Peeling, E. and Tucker, A., Consensus gene regulatory networks: combining multiple microarray gene expression datasets, 3rd International Symposium on Computational Life Science, AIP Conference Proceedings (940) : 38- 49

    (2007) Swift, S., Tucker, A. and Hirsch, M., Improving the performance of consensus clustering through seeding: an application to visual field data, The Intelligent Data Analysis in Biomedicine and Pharmacology Workshop (IDAMAP2007)

    (2007) Peeling, E., Tucker, A. and 't Hoen, PAC., Discovery of local regulatory structure from microarray gene expression data using Bayesian networks, Intelligent Data Analysis in Medicine and Pharmacology

    (2007) Swift, S., Tucker, A. and Crampton, J., An improved restricted growth function genetic algorithm for the consensus clustering of retinal nerve fibre data, The Genetic and Evolutionary Computation Conference (GECCO 2007)

    (2007) Tucker, A., Swift, S. and Crampton, J., Efficiency updates for the restricted growth function GA for grouping problems, GECCO 2007 - the Genetic and Evolutionary Computation Conference

    (2007) Peeling, E. and Tucker, A., Making time: pseudo time-series for the temporal analysis of cross section data, Lecture Notes in Computer Science (IDA 2007)

    (2006) Hirsch, M., Tucker, A., Swift, S., Martin, N., Orengo, C., Kellam, P. and Liu, X., Improved robustness in time series analysis of gene expression data by polynomial model based clustering, The Second International Symposium on Computational Life Science (COMPLIFE'06), Lecture Notes in Computer Science (4216/2006) : 1- 10

    (2005) Tucker, A., Vinciotti, V., 't Hoen, PAC. and Liu, X., Bayesian network classifiers for time series microarray data, 6th International Symposium on Intelligent Data Analysis (IDA-2005), Lecture Notes in Computer Science (3646) : 475- 485

    (2005) Swift, S., Shi, A., Crampton, J. and Tucker, A., ICARUS: intelligent coupon allocation for retailers using search, IEEE Congress on Evolutionary Computation (CEC-2005)

    (2005) Counsell, S., Swift, S., Tucker, A. and Mendes, E., Object-oriented cohesion as a surrogate of software comprehension: an empirical study, 5th IEEE International Workshop on Source Code Analysis and Manipulation (SCAM 2005)

    (2004) Vinciotti, V., Tucker, A., Liu, X., Panteris, E. and Kellam, P., Identifying genes with high confidence from small samples, Workshop on Data Mining in Functional Genomics and Proteomics: Current Trends and Future Directions at the European Conference in Artificial Intelligence (ECAI 2004)

    (2004) Tucker, A., Vinciotti, V., Liu, X. and Garway-Heath, D., Bayesian networks to classify visual field data, The Association for Research in Vision and Ophthalmology Annual Conference (ARVO 2004)

    (2004) Sheng, W., Tucker, A. and Liu, X., Clustering with niching genetic k-means algorithm, GECCO-2004 - the Genetic and Evolutionary Computation Conference, Lecture Notes in Computer Science (3103) : 162- 173

    (2003) Tucker, A., Garway-Heath, D. and Liu, X., Bayesian classification and forecasting of visual field deterioration, The Ninth Workshop on Intelligent Data Analysis in Medicine and Pharmacology and Knowledge-Based Information Management in Anaesthesia and Intensive Care

    (2003) Tucker, A. and Liu, X., Learning dynamic bayesian networks from multivariate time series with changing dependencies, The Fifth International Conference on Intelligent Data Analysis (IDA-2003), Lecture Notes on Computer Science (2810) : 100- 110

    (2003) Counsell, S., Liu, X., Najjar, R., Swift, S. and Tucker, A., Applying intelligent data analysis to coupling relationships in object-oriented software, The Fifth International Symposium on Intelligent Data Analysis (IDA-2003), Lecture Notes on Computer Science (2810) : 440- 450

    (2003) Tucker, A., Garway-Heath, D. and Liu, X., Spatial operators for evolving dynamic probabilistic networks from spatio-temporal data, The Genetic and Evolutionary Computation Conference, Lecture Notes in Computer Science (2723) : 2360- 2371

    (2001) Counsell, S., Liu, X., McFall, J., Swift, S. and Tucker, A., Using evolutionary algorithms to tackle large scale grouping problems: an application to email log file data, Proceedings of the Late-Breaking Papers of the Genetic and Evolutionary Computation Conference (GECCO-2001)

    (2001) Swift, S., Tucker, A., Martin, N. and Liu, XH., Grouping multivariate time series variables: applications to chemical process and visual field data, 20th SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence (ES2000), Knowledge Based Systems (14) : 147- 154

    (2001) Counsell, S., Liu, X., McFall, J., Swift, S. and Tucker, A., Optimising the grouping of email users to servers using intelligent data analysis, International Conference on the Engineering of Information Systems (EIS-2001)

    (2001) Kellam, P., Liu, X., Martin, N., Orengo, C., Swift, S. and Tucker, A., A framework for modelling short, high-dimensional multivariate time series: preliminary results in virus gene expression data analysis, The Fourth International Symposium on Intelligent Data Analysis (IDA-2001), Lecture Notes on Computer Science (2189) : 218- 227

    (2001) Counsell, S., Swift, S. and Tucker, A., An empirical investigation into the interpretation of faults in requirements documents, The Empirical Assessment in Software Engineering (EASE-2001)

    (2001) Liu, X., Swift, S. and Tucker, A., Using evolutionary algorithms to tackle large scale grouping problems, The Genetic and Evolutionary Computation Conference (GECCO-2001)

    (2001) Kellam, P., Liu, X., Martin, N., Orengo, C., Swift, S. and Tucker, A., Comparing, contrasting and combining clusters in viral gene expression data, The Intelligent Data Analysis in Medicine and Pharmacology Workshop (IDAMAP-2001)

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    Code and Data

    ARHMM Data and Code. This link contains ARHMM datasets and code in MATLAB generated for testing pseudo time-series algorithms.
    Consensus Clustering Code in R . This link points to the directory for the consensus clustering functions in R.
    RGFGA VAR Data . This link contains several datasets generated using the Vector Autoregressive model for testing grouping algorithms for Multivariate Time Series.