Department of Computer Science
Allan Tucker - Senior Lecturer
I have advised the UK thinktank REFORM on the Use of AI in the NHS
Also advised on the Wellcome Trust on the ethical implications of the use of AI in health and medical research
and the PHG Foundation on Regulating algorithms in healthcare:
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
E-Mail : email@example.com
Telephone : 44 (0)1895 266933
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.
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:
Previous PhD students:
I have secured grants from
I am or have previously been on the Program committee of
I have reviewed for numerous journals including:
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
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
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.