Skip to main content

Research Repository

Advanced Search

Professor John McCall


Temporal patterns in artificial reaction networks. (2012)
Conference Proceeding
GERRARD, C., MCCALL, J., COGHILL, G.M. and MACLEOD, C. 2012. Temporal patterns in artificial reaction networks. In Villa, A.E.P., Duch, W., √Črdi, P., Masulli, F. and Palm, G. (eds.) Artificial neural networks and machine learning: proceedings of the 22nd International conference on artificial neural networks (ICANN 2012), 11-14 September 2012, Lausanne, Switzerland. Lecture notes in computer science, 7552. Berlin: Springer [online], part I, pages 1-8. Available from: https://doi.org/10.1007/978-3-642-33269-2_1

The Artificial Reaction Network (ARN) is a bio-inspired connectionist paradigm based on the emerging field of Cellular Intelligence. It has properties in common with both AI and Systems Biology techniques including Artificial Neural Networks, Petri N... Read More about Temporal patterns in artificial reaction networks..

Problem dependent metaheuristic performance in Bayesian network structure learning. (2012)
Thesis
WU, Y. 2012. Problem dependent metaheuristic performance in Bayesian network structure learning. Robert Gordon University, PhD thesis.

Bayesian network (BN) structure learning from data has been an active research area in the machine learning field in recent decades. Much of the research has considered BN structure learning as an optimization problem. However, the finding of optimal... Read More about Problem dependent metaheuristic performance in Bayesian network structure learning..

Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers. (2011)
Journal Article
REGNIER-COUDERT, O., MCCALL, J., LOTHIAN, R., LAM, T., MCCLINTON, S. and N'DOW, J. 2012. Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers. Artificial intelligence in medicine [online], 55(1), pages 25-35. Available from: https://doi.org/10.1016/j.artmed.2011.11.003

Objectives: Prediction of prostate cancer pathological stage is an essential step in a patient's pathway. It determines the treatment that will be applied further. In current practice, urologists use the pathological stage predictions provided in Par... Read More about Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers..

Artificial reaction networks. (2011)
Presentation / Conference
GERRARD, C.E., MCCALL, J., COGHILL, G.M. and MACLEOD, C. 2011. Artificial reaction networks. Presented at the 11th UK workshop on computational intelligence (UKCI 2011), 7-9 September 2011, Manchester, UK.

In this paper we present a novel method of simulating cellular intelligence, the Artificial Reaction Network (ARN). The ARN can be described as a modular S-System, with some properties in common with other Systems Biology and AI techniques, including... Read More about Artificial reaction networks..

A sequence-length sensitive approach to learning biological grammars using inductive logic programming. (2011)
Thesis
MAMER, T. 2011. A sequence-length sensitive approach to learning biological grammars using inductive logic programming. Robert Gordon University, PhD thesis.

This thesis aims to investigate if the ideas behind compression principles, such as the Minimum Description Length, can help us to improve the process of learning biological grammars from protein sequences using Inductive Logic Programming (ILP). Con... Read More about A sequence-length sensitive approach to learning biological grammars using inductive logic programming..

Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm. (2009)
Thesis
BROWNLEE, A.E.I. 2009. Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm. Robert Gordon University, PhD thesis.

A well-known paradigm for optimisation is the evolutionary algorithm (EA). An EA maintains a population of possible solutions to a problem which converges on a global optimum using biologically-inspired selection and reproduction operators. These alg... Read More about Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm..

An inductive logic programming approach to learning which uORFs regulate gene expression. (2008)
Thesis
SELPI 2008. An inductive logic programming approach to learning which uORFs regulate gene expression. Robert Gordon University, PhD thesis.

Some upstream open reading frames (uORFs) regulate gene expression (i.e. they are functional) and can play key roles in keeping organisms healthy. However, how uORFs are involved in gene regulation is not het fully understood. In order to get a compl... Read More about An inductive logic programming approach to learning which uORFs regulate gene expression..

Solving the Ising spin glass problem using a bivariate EDA based on Markov random fields. (2006)
Conference Proceeding
SHAKYA, S.K., MCCALL, J.A.W. and BROWN, D.F. 2006. Solving the Ising spin glass problem using a bivariate EDA based on Markov random fields. In Proceedings of the 2006 IEEE congress on evolutionary computation (CEC 2006), 16-21 July 2006, Vancouver, Canada. New York: IEEE [online], article number 1688408, pages 908-915. Available from: https://doi.org/10.1109/CEC.2006.1688408

Markov Random Field (MRF) modelling techniques have been recently proposed as a novel approach to probabilistic modelling for Estimation of Distribution Algorithms (EDAs). An EDA using this technique was called Distribution Estimation using Markov Ra... Read More about Solving the Ising spin glass problem using a bivariate EDA based on Markov random fields..

DEUM: a framework for an estimation of distribution algorithm based on Markov random fields. (2006)
Thesis
SHAKYA, S.K. 2006. DEUM: a framework for an estimation of distribution algorithm based on Markov random fields. Robert Gordon University, PhD thesis.

Estimation of Distribution Algorithms (EDAs) belong to the class of population based optimisation algorithms. They are motivated by the idea of discovering and exploiting the interaction between variables in the solution. They estimate a probability... Read More about DEUM: a framework for an estimation of distribution algorithm based on Markov random fields..

Incorporating a metropolis method in a distribution estimation using Markov random field algorithm. (2005)
Conference Proceeding
SHAKYA, S.K., MCCALL, J.A.W. and BROWN, D.F. 2005. Incorporating a metropolis method in a distribution estimation using Markov random field algorithm. In Proceedings of the 2005 IEEE congress on evolutionary computation (CEC 2005), 2-5 September 2005, Edinburgh, UK. New York: IEEE [online], volume 3, article number 1555017, pages 2576-2583. Available from: https://doi.org/10.1109/CEC.2005.1555017

Markov Random Field (MRF) modelling techniques have been recently proposed as a novel approach to probabilistic modelling for Estimation of Distribution Algorithms (EDAs)[34, 4]. An EDA using this technique, presented in [34], was called Distribution... Read More about Incorporating a metropolis method in a distribution estimation using Markov random field algorithm..