Brunel Allan Tucker

Department of Computer Science

Allan Tucker - Senior Lecturer

Head of:


I have advised the UK thinktank REFORM on the Use of AI in the NHS
Report Here

Also advised on the Wellcome Trust on the ethical implications of the use of AI in health and medical research
Report Here

and the PHG Foundation on Regulating algorithms in healthcare:
Report Here

I am associate editor for BMC Medical Informatics and Decision Making

I am on the editorial board for Journal of Biomedical Informatics

I am a member of the UK Health Data Analytics Network

I am currently chair for the AIME 2021 conference, Porto

I am advising the MHRA and CPRD on regulating the use of AI

Contact Information
E-Mail :
Telephone : 44 (0)1895 266933
Official Homepage.

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 senior lecturer at Brunel University, heading the Intelligent Data Analysis Research group.

Current projects:

  • Sub-catgeories of disease for patient-specific diagnosis (Awad Alyousef PhD student, Pietro Bosoni with University of Pavia)
  • Non-stationary modelling of disease progression (Lilly Youssefi PhD student)
  • Pseudo time-series trajectory modelling for integrating longitudinal and cross-sectional data (Tom Nealon PhD student with Birkbeck College and Leiden University Medical Centre; Chris Beyer PhD student with Magdeburg University)
  • Dynamics of fish populations in the Northern Atlantic using Hidden Markov Models (Neda Trifonova PhD student; Laura Uuisitalo MSc project with University of Helsinki)
  • Biodiversity informatics for hereogenous data (Nicky Nicholson PhD student)
  • Extreme events in a changing climate: a Big Data perspective (Nathanial Harwood PhD student)
  • A multi-dimensional environment-health risk analysis system for Kazakhstan (British Council Institutional Links Grant)

    My projects involve collaborations with Moorfield’s Eye Hospital; Royal Free Hospital, Hampstead; Royal Botanical Gardens, Kew; Leiden University Medical Centre, University of Pavia, Magdeburg University, DEFRA and the Canadian Dept of Fisheries and Oceans.

    Current PhD students:

  • Lilly Youssefi (First supervisor): (Self funded)
  • Joanna Pawlik (First supervisor): (Self funded)
  • Awad Alyousef (First supervisor): (Self funded)
  • Erfan Sahadi (First supervisor): (EPSRC funded)
  • Ben Evans (First supervisor): (NERC funded)
  • Toyah Overton (First supervisor): (Self funded)

    Previous PhD students:

  • STEELE Emma (First supervisor): "Combining Heterogeneous Sources of Data for the Reverse-Engineering of Gene Regulatory Networks" (Completed 2009, Now at Open University)
  • HASSAN Fadratul (First supervisor): "Using Uniform Crossover to Refine Simulated Annealing Solutions for the Automatic Design of Spatial Layouts" (2013, Now at University of Malaysia)
  • ANVAR Yahya (Co-Promoter with Leiden University Medical Centre, Netherlands): "Converging models for interspecies transcriptome studies of human diseases" (2012, Now at Leiden University)
  • CECCON Stefano (First supervisor): "The Identification and Modelling of Key Stages in a Temporal Process" (2013, Isambard Scholarship, now at The Times newspaper)
  • LI Yuanxi (First supervisor): "The Exploitation of Cross-Sectional Studies to Infer Disease Processes" (2014, self funded, now at UCL)
  • FRANCO Chiara (Second supervisor): "Modelling the dynamics of CaCO3 budgets in changing environments using a Bayesian belief network approach" (2014)
  • BO Valeria (First supervisor): "The Integration of Multiple Data Sources for Building Robust Gene Regulatory Networks" (2015, departmental EPSRC grant, now at Cambridge University)
  • TRIFONOVA Neda (First supervisor): "" (2016, NERC funded, now starting at University of Miami in Jan 2017)
  • NICHOLSON Nicky (First supervisor):
  • HARWOOD Nathanial (First supervisor):
  • AL LUHAYBI Mashael (First Supervisor):

    I have secured grants from

  • Innovate UK
  • British Council
  • NERC
  • National Grid
  • The Royal Society

    I am or have previously been on the Program committee of

  • IEEE Computational Based Medical Systems,
  • American Association for Medical Informatics (Joint Summits),
  • 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,
  • 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 am currently General Chair for AIME 2021
  • I am Advisory Chair for IDA 2021
  • I was program co-chair for IDA 2017.
  • I was program co-chair for IEEE CBMS 2017.
  • I was general chair for IDA 2013 in London and was program chair for IDA 2012 in Helsinki
  • I was co-chair for IDAMAP 2007 in Amsterdam, IDAMAP 2008 in Washington, US, IDAMAP 2011 in Bled, Slovenia,
  • 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:

    Trajectories through the disease process, University of Porto, 2017
    Intelligent Data Analytics: Three Algorithms Inspired by Data from Life Sciences, University of Manchester, 2016
    Trajectories through the disease process, University of Pavia, Institute of Population Health, Manchester, 2016
    What is Big Data?, University of the 3rd Age, Royal Society, 2016
    What is the Fuss about Big Data? Is it a 'Gold Rush'?, BCS, 2015
    Artificial Intelligence: Useful Tool or Robot Overlords?, SciBar, 2015
    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 pubs in 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


    Journal Papers

    Please see full list on my official homepage.

    (2019) Maldonado, AD., Uusitalo, L., Tucker, A., Blenckner, T., Aguilera, PA. and Salmerón, A. (2019) 'Prediction of a complex system with few data: Evaluation of the effect of model structure and amount of data with dynamic bayesian network models'. Environmental Modelling and Software, 118. pp. 281 - 297. ISSN: 1364-8152

    (2019) Jilani, MZMB., Tucker, A. and Swift, S. (2019) 'An application of generalised simulated annealing towards the simultaneous modelling and clustering of glaucoma'. Journal of Heuristics, 25 (6). pp. 933 - 957. ISSN: 1381-1231

    (2019) Scutari, M., Vitolo, C. and Tucker, A. (2019) 'Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation'. Statistics and Computing, 29 (5). pp. 1095 - 1108. ISSN: 0960-3174

    (2018) Russell, A., Ghalaieny, M., Gazdiyeva, B., Zhumabayeva, S., Kurmanbayeva, A., Akhmetov, KK., et al. (2018) 'A Spatial Survey of Environmental Indicators for Kazakhstan: An Examination of Current Conditions and Future Needs'. International Journal of Environmental Research, 12 (5). pp. 735 - 748. ISSN: 1735-6865

    (2018) Alyousef, AA., Nihtyanova, S., Denton, C., Bosoni, P., Bellazzi, R. and Tucker, A. (2018) 'Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease'. Journal of Healthcare Informatics Research, 2 (4). pp. 402 - 422. ISSN: 2509-4971

    (2018) Uusitalo, L., Tomczak, MT., Müller-Karulis, B., Putnis, I., Trifonova, N. and Tucker, A. (2018) 'Hidden variables in a Dynamic Bayesian Network identify ecosystem level change'. Ecological Informatics, 45 (May 2018). pp. 9 - 15. ISSN: 1574-9541

    (2018) Vitolo, C., Scutari, M., Ghalaieny, M., Tucker, A. and Russell, A. (2018) 'Modelling air pollution, climate and health data using Bayesian Networks: a case study of the English regions'. Earth and Space Science, 5 (4). pp. 76 - 88. ISSN: 2333-5084

    (2018) Curtis, TY., Bo, V., Tucker, A. and Halford, NG. (2018) 'Construction of a network describing asparagine metabolism in plants and its application to the identification of genes affecting asparagine metabolism in wheat under drought and nutritional stress'. Food and Energy Security, 7 (1). pp. e00126 - e00126. ISSN: 2048-3694

    (2017) Chudasama, D., Bo, V., Hall, M., Anikin, V., Jeyaneethi, J., Gregory, J., et al. (2017) 'Identification of novel cancer biomarkers of prognostic value using specific gene regulatory networks (GRN): a novel role of RAD51AP1 for ovarian and lung cancers'. Carcinogenesis, 39 (3). pp. 407 - 417. ISSN: 1460-2180

    (2017) Nicolson, N., Challis, K., Tucker, A. and Knapp, S. 'Impact of e-publication changes in the International Code of Nomenclature for algae, fungi and plants (Melbourne Code, 2012) - did we need to "run for our lives"?'. BMC evolutionary biology, 17 (1). pp. 116 - 116. ISSN: 1471-2148

    (2017) Tucker, A., Li, Y. and Garway-Heath, D. 'Updating Markov models to integrate cross-sectional and longitudinal studies'. Artificial Intelligence in Medicine, 77. pp. 23 - 30. ISSN: 0933-3657

    (2017) Tucker, A., Trifonova, N., Maxwell, D., Pinnegar, J. and Kenny, A. 'Predicting ecosystem responses to changes in fisheries catch, temperature, and primary productivity with a dynamic Bayesian network model'. ICES Journal of Marine Science, 73 (10).

    (2016) Vitolo, C. , Russell, A. and Tucker, A. 'RDEFRA: Interact with the UK AIR Pollution Database from DEFRA'. The Journal of Open Source Software, 1 (4). doi: 10.21105/joss.00051

    (2016) Franco, C. , Hepburn, L. , Smith, D. , Nimrod, S. and Tucker, A. 'A Bayesian Belief Network to assess rate of changes in coral reef ecosystems'. Environmental Modelling and Software. doi: 10.1016/j.envsoft.2016.02.029

    (2015) Trifonova, N. , Kenny, A. , Maxwell, D. , Duplisea, D. , et al. 'Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology'. Ecological Informatics, 30 pp. 142 - 158. doi: 10.1016/j.ecoinf.2015.10.003

    (2015) Al Nasseri, AA. , Tucker, A. and de Cesare, S. 'Quantifying StockTwits Semantic Terms' Trading Behavior in Financial Markets: An Effective Application of Decision Tree Algorithms'. Expert Systems with Applications. doi: 10.1016/j.eswa.2015.08.008 Download publication

    (2014) Bo, V. Curtis, T. Lysenko, A. Saqi, M. Swift, S. and Tucker, A. Discovering Study-Specific Gene Regulatory Networks, PLOS ONE.

    (2014) 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) 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

    (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) 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

    (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

    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.
    Pseudo Time Data and Code. This link contains code in R to generate pseudo time series from simulated data.