Computational Intelligence Group
The Group focuses on the intersection between Computer Science, Biology and Psychology. We are interested in how biological and psychological systems can be modelled using computation and how biological systems can inspire new forms of computation. We also examine both how this novel computation and how the application of psychology can be used in practical setttings such as data mining and visualization.
The interdisciplinary nature of the work in the group means that many of the members are active in the Centre for BioMedical Informatics, a university-wide virtual Centre that aims to foster collaborative research and postgraduate teaching in the broad area of Biomedical Informatics.
Our work is consistently published in highly ranked journals, and has been supported by a number of research grants. A full list of the group's papers.
Go straight to:
- Modelling Biological Systems
- Cognitive Systems
- Bioinspired Computing
- Data Mining and Bioinformatics
- Information Visualization
Modelling Biological Systems
Our interests include modelling biological systems at various levels, which can greatly improve the understanding of such systems. In particular, recent work has concentrated on computational modelling of bacterial Fimbriae which allow bacterial parasites to attach themselves to host cells. We have also, alongside biologists, investigated the computational properties of gene regulatory networks. Gene networks have attracted much recent interest due to the considerable amount of data being made available through new experimental techniques.
Working with the School of Biosciences, we have also developed software to assist in the analysis of NMR relaxation data.
The advent of propriety pulse sequences from the major NMR manufacturers has made the acquisition of 15N NMR relaxation data easy, but basic properies can be non-trivial to deduce. The RPLOT program enables the user to easily deduce global correlation time and identify individual 15N nuclei exhibiting Rex line broadening behaviour.
Introduction to Modeling for Biosciences. David J. Barnes and Dominique Chu. Springer, August 2010
- Computational limits to binary genes. Nicolae Radu Zabet and Dominique F. Chu. J. R. Soc. Interface, 7:945-954, June 2010
- Modeling fimbriae mediated parasite-host interactions. D. Chu and D. Barnes. Journal of Theoretical Biology, 264(4), May 2010
- A Theoretical Interpretation of the Transient Sialic Acid Toxicity of a nanR Mutant of Escherichia coli. D Chu, J Roobol, and I C Blomfield. Journal of Molecular Biology, 375:875-889, January 2008
Recent Research Funding:
- EPSRC: Evolution of group properties via individual level selection
- Norwegian Research Council: Reflexive Systems Biology: Towards an appreciation of biological, scientific and ethical complexity
Contact: David Barnes
Virtual P3 ERPimages for targets seen outside and inside the AB
From Attention Increases the Temporal Precision of Conscious Perception
Our research explores how the mind emerges from the brain to generate a spectrum of cognitive capacities. This involves studying perception, consciousness, attention, language, emotions and decision-making using a mixture of methods, including behavioural and electrophysiological (EEG) experimentation and connectionist and symbolic modelling. We also undertake applied neuroscience research focussed on brain-computer and human-computer interfaces.
- Attention Increases the Temporal Precision of Conscious Perception: Verifying the Neural-ST2 Model. Srivas Chennu, Patrick Craston, Brad Wyble, and Howard Bowman. PLoS Computational Biology, 5(11):e1000576, November 2009.
- The attentional blink reveals serial working memory encoding: Evidence from virtual & human event-related potentials. Patrick Craston, Brad Wyble, Srivas Chennu, and Howard Bowman. Journal of Cognitive Neuroscience, 21(3):550-566, March 2009.
- Neural correlates of intrusion of emotion words in a modified Stroop task. J. C. van Hooff, K. C. Dietz, D. Sharma, and H. Bowman. International Journal of Psychophysiology, 67(1):23-34, January 2008.
- On the fringe of awareness: The glance-look model of attention-emotion interactions. Li Su, Philip Barnard, and Howard Bowman. In K. Diamantaras, W. Duch, and L.S. Iliadis, editors, International Conference on Artificial Neural Networks, volume 6354 of Lecture Notes in Computer Science, pages 504-509. Springer-Verlag, July 2010.
Contact: Howard Bowman
This work finds effective solutions to hard problems by using strategies inspired by biological systems. Examples of bioinspired techniques are evolutionary algorithms, neural networks, swarm intelligence methods and artificial immune systems. We have applied such techniques to data analysis, music technology, pure mathematics, robotics, multimodal optimization, and bioinformatics
We have a particular interest in genetic programming methods where the aim is to create software from descriptions of required functionality rather than by stating exactly what programs should do. We are looking how to combine software engineering and program analysis methods with automatic programming techniques to produce rigorous programs in an automatic fashion. We are also interested in how different soft computing techniques, such as artificial immune systems, can be used for genetic programming.
- Semantic analysis of program initialisation in genetic programming. Lawrence Beadle and Colin G Johnson. Genetic Programming and Evolvable Machines, 10(3):307-337, September 2009.
- Particle swarm for attribute selection in bayesian classification: an application to protein function prediction. Elon S Correa, Alex A Freitas, and Colin G Johnson. Journal of Artificial Evolution and Applications, 2008:12 pages, May 2008
- cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes. F.E.B. Otero, A.A. Freitas, and C.G. Johnson. In M. Dorigo et al., editor, Ant Colony Optimization and Swarm Intelligence (Proc. ANTS 2008), LNCS 5217, pages 48-59. Springer, September 2008
Recent Research Funding:Colin Johnson
Data Mining and Bioinformatics
We have explored a number of novel approaches to data mining, including evolutionary algorithms, swarm intelligence (mainly ant colony optimization) and new hierarchical classification methods. This work has been applied to knowledge discovery in a number of application areas, particularly protein function prediction, and in the increasingly important area of studying ageing in humans.
Recent Research Funding:
- EPSRC: A Synergistic Integration of Natural and Artificial Immunology for the Prediction of Hierarchical Protein Functions
- A data mining approach for classifying DNA repair genes into ageing-related or non-ageing related. A.A. Freitas, O. Vasieva and J.P. de Magalhaes. BMC Genomics, 12:27, January 2011
- Automating the Design of Data Mining Algorithms: an Evolutionary Computation Approach. G.L.Pappa and A.A. Freitas. Springer, 2010, 187 pages
- Evolving rule induction algorithms with multi-objective grammar-based genetic programming. G.L. Pappa and A.A. Freitas. Knowledge and Information Systems, 19(3):283-309, June 2009
- Automatically evolving rule induction algorithms tailored to the prediction of postsynaptic activity in proteins. G.L. Pappa and A.A. Freitas. Intelligent Data Analysis, 13(2):243-259, May 2009
Euler diagrams are unique in being able to intuitively represent intersections and containment of sets. They are in common use where visual representations of logic are required, but are also used visualize the outcome of experiments in medicine and biosciences. We have developed novel methods to automatically visualize area proportional Euler diagrams, and have developed the first technology that allows any abstract description to be visualized as an Euler diagram.
- Automatic Metro Map Layout Using Multicriteria Optimization. Jonathan Stott, Peter Rodgers, Juan Carlos Martinez-Ovando, and Stephen G. Walker. Transactions on Visualization and Computer Graphics, 16(1):101-114, January 2011
- Drawing Area-Proportional Euler Diagrams Representing Up To Three Sets. Peter Rodgers, Gem Stapleton, Jean Flower and John Howse. IEEE Transactions on Visualization and Computer Graphics, 20 (1). pp. 56-69. ISSN 1077-2626.2014
- The Impact of Shape on the Perception of Euler Diagrams. Andrew Blake, Gem Stapleton, Peter Rodgers, Liz Cheek, and John Howse. In Diagrams, LNCS(LNAI) 8578, pages 124-138. Springer, July 2014
- Exploring Local Optima in Schematic Layout.Daniel Chivers and Peter Rodgers. Proceedings of The 19th International Conference on Distributed Multimedia Systems (DMS 2013) International Workshop on Visual Languages and Computing (VLC 2012), 8-10 August 2013, Brighton, UK
Recent Research Funding:Peter Rodgers