Papers

2008 (or in press)

You, L., Brusic, V. L., Gallagher, M. and Bodén, M. Using Gaussian process with test rejection to detect T-cell epitopes in pathogen genomes. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Accepted.

Bodén, M. and Bailey, T. L. Associating transcription factor binding site motifs with target GO terms and target genes. Nucleic Acids Research, 36(12), pp. 4108-4117.

Buske, F. A., Maetschke, S. and Bodén, M. It's about time: signal recognition in staged models of protein translocation. Pattern Recognition, Accepted.

Bauer, D., Buske, F. A. and Bodén, M. Predicting SUMOylation Sites. In Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2008), Springer Lecture Notes in Computer Science, vol. 5265, pp. 28-40.

Bodén, M. and Teasdale, R. D. Determining nucleolar association from sequence by leveraging protein-protein interactions. Journal of Computational Biology, 15(3): 291-304, 2008.

Bodén, M., Predicting nucleolar proteins using support-vector machines, in Proceedings of the 6th Asia-Pacific Bioinformatics Conference – APBC 2008, Brazma, A., Miyano, S. and Akutsu, T. (eds.), pp. 19-28, Imperial College Press, 2008.

2007

Buske, F. and Bodén, M. Decoupling signal recognition from sequence models of protein secretion, in Proceedings of 2007 International Symposium on Computational Models for Life Sciences – CMLS’07, Pham and Zhou (eds), pp. 147-156.

Dufton, L. and Bodén, M. Reducing the number of support vectors to allay inefficiency of large-scale models in computational biology, in Proceedings of 2007 International Symposium on Computational Models for Life Sciences – CMLS’07, Pham and Zhou (eds), pp. 340-348.

Tino, P., Hammer, B. and Bodén, M. Markovian bias of neural-based architectures with feedback connections, Perspectives of Neural-Symbolic Integration, pp. 95-133, Hitzler and Hammer (eds.), Springer Verlag.

You, L., Zhang, P., Bodén, M. and Brusic, V. L. Understanding Prediction Systems for HLA-Binding Peptides and T-cell Epitope Identification, in Proceedings of the 2nd IAPR Workshop on Pattern Recognition in Bioinformatics, LNBI 4774, pp. 337-348, Singapore, Springer Verlag, 2007.

Hawkins, J., Mahony, D., Maetschke, S., Wakabayashi, M., Teasdale, R.D., and Bodén, M., Identifying Novel Peroxisomal Proteins. Proteins: Structure, Function and Bioinformatics. 69(3), pp. 606-616. 2007.

Hawkins, J., Davis, L., and Bodén, M., Predicting Nuclear Localization, Journal of Proteome Research. 6(4), 1402-1409, 2007.

Maetschke, S., Gallagher, M. and Bodén, M. A Comparison of Sequence Kernels for Localization Prediction of Transmembrane Proteins. In Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 367-372, 2007.

Suksawatchon, J., Lursinsap, C. and Bodén, M., Computing the Reversal Distance Between Genomes in the Presence of Multi-Gene Families via Binary Integer Programming. Journal of Bioinformatics and Computational Biology, 5(1), 117-133, 2007.

Bodén, M. and Bodén, M. Evolving spelling exercises to suit individual student needs. Applied Soft Computing. 7, pp. 126-135, 2007.

2006

Davis, L., Hawkins, J., Maetschke, S. and Bodén, M., Comparing SVM sequence kernels: A protein subcellular localization theme, In Proceedings of the Workshop on Intelligent Systems for Bioinformatics, CRPIT (vol. 73), 2006.

Maetschke, S., Bodén, M. and Gallagher, M., Higher order HMMs for Localization Prediction of Transmembrane Proteins, In Proceedings of the Workshop on Intelligent Systems for Bioinformatics, CRPIT (vol. 73), 2006.

Hawkins, J. and Bodén, M., Multi-stage Redundancy Reduction: Effective Utilisation of Small Protein Data Sets, In Proceedings of the Workshop on Intelligent Systems for Bioinformatics, CRPIT (vol. 73), 2006.

Bauer, D., Bodén, M., Thier, R. and Gillam, E. M. STAR: Predicting recombination sites from amino acid sequence. BMC Bioinformatics, 7:437, 2006. doi:10.1186/1471-2105-7-437.

Bodén, M. and Bailey, T. L. Identifying sequence regions undergoing conformational change via predicted continuum secondary structure, Bioinformatics. 22(15):1809-1814. 2006.

Bodén, M., Yuan, Z. and Bailey, T. L. Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures. BMC Bioinformatics. 7:68, 2006.

Bodén, M. and Hawkins, J. Evolving discriminative motifs for recognizing proteins imported to the peroxisome via the PTS2 pathway, IEEE Congress on Evolutionary Computation, 2006.

Yuan, Z., Zhang, F., Davis, M. J., Bodén, M. and Teasdale, R. D. Predicting the solvent accessibility of transmembrane residues from protein sequence. Journal of Proteome Research. 5(5), pp. 1063-1070, 2006.

DOI

Hawkins, J. and Bodén, M. Detecting and sorting targeting peptides with recurrent networks and support vector machines. Journal of Bioinformatics and Computational Biology, 4(1), pp. 1-18, 2006.

2005

Bauer, D., Bodén, M., Thier, R. and Yuan, Z. Predicting structural disruption of proteins caused by crossover. In Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 514-520. San Diego, 2005.

Hawkins, J. and Bodén, M. Predicting Peroxisomal proteins. In Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 469-474. San Diego, 2005.

Suksawatchon, J., Lursinsap, C. and Bodén, M., Heuristic Algorithm for Computing Reversal Distance with MultiGene Families via Binary Integer Programming. In Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 187-193. San Diego, 2005.

Bodén, M. and Hawkins, J. Prediction of subcellular localisation using sequence-biased recurrent networks. Bioinformatics. 21(10), pp. 2279-2286, 2005.

Wakabayashi, M., Hawkins, J., Maetschke, S. and Bodén, M., Exploiting sequence dependencies in the prediction of peroxisomal proteins. In Intelligent Data Engineering and Automated Learning - IDEAL 2005, pp. 454-461, 2005.

Hawkins, J. and Bodén, M. The Applicability of Recurrent Neural Networks for Biological Sequence Analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2(3), pp. 243-253, 2005.

Bodén, M. and Hawkins, J. Improved access to sequential motifs: A note on the architectural bias of recurrent networks. IEEE Transactions on Neural Networks. 16(2), 491-494, 2005.

Bodén, M. and Hawkins, J. Detecting residues in targeting peptides. In Proceedings of the Asia-Pacific Bioinformatics Conference, pp. 131-140. Imperial College Press. Singapore. 2005.

Maetschke, S., Towsey, M. and Bodén, M. BLOMAP: An encoding of amino acids which improves signal peptide cleavage prediction. In Proceedings of the Asia-Pacific Bioinformatics Conference, pp. 141-150. Imperial College Press. Singapore. 2005.

Older

Bodén, M. Generalization by symbolic abstraction in cascaded recurrent networks, Neurocomputing, 57, pp. 87-104. 2004.

Bodén, M. Using evolutionary noise to improve prediction of rapidly evolving targeting peptides, IEEE Congress on Evolutionary Computation, Canberra. 2003.

Bodén, M. and Blair, A. Learning the dynamics of embedded clauses, Applied Intelligence: Special issue on natural language and machine learning, 19(1/2), pp. 51-63. 2003.

Bodén, M. Generalization by structural properties from sparse nested symbolic data, Proceedings of ESANN 2002 -- Special session on Perspectives on Learning with Recurrent Networks. 2002.

Bodén, M. and Wiles, J. On learning context free and context sensitive languages, IEEE Transactions on Neural Networks. 13(2), pp. 491-493. 2002.

Wiles, J., Blair, A. and Bodén, M. Representation Beyond Finite States: Alternatives to Push-Down Automata, in A Field Guide to Dynamical Recurrent Networks, Kolen, J. F. and Kremer, S. C. (eds.), pp. 129-142, IEEE Press. 2001.

Bodén, M. and Wiles, J. Context-free and context-sensitive dynamics in recurrent neural networks, Connection Science, 12 (3/4), pp. 197-210. 2000.

Bodén, M., Jacobsson, H. and Ziemke, T. Evolving context-free language predictors, in GECCO-2000: Proceedings of the Genetic and Evolutionary Computation Conference. 2000.

 .Z

Bodén, M. and Niklasson, L. Semantic Systematicity and Context in Connectionist Networks, Connection Science, 12 (2), pp. 111-142. 2000.

Bodén, M., Jacobsson, H. and Ziemke, T. Evolving recurrent networks for context-free language prediction, presented at the Workshop for Evolutionary Computation in Cognitive Science (ECCS), Melbourne. 2000.

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Bodén, M., Wiles, J., Tonkes, B. and Blair, A. Learning to predict a context-free language: Analysis of dynamics in recurrent hidden units, in Proceedings of ICANN 99, pp. 359-364. Edinburgh, IEE. 1999.

Bodén, M., Wiles, J., Tonkes, B. and Blair, A. On the ability of recurrent nets to learn deeply embedded structures, presented at the IJCAI 99 workshop on Sequence learning, Stockholm. 1999.

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Niklasson, L. and Bodén, M. Content, Context and Connectionist Networks, in Proceedings of the 21st annual meeting of the Cognitive Science Society,pp. 474-479. Lawrence Erlbaum Associates. 1999.

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Ziemke, T., Carlsson, J. and Bodén, M. An experimental comparison of weight evolution in neural control architectures for a 'garbage-collecting' Khepera robot, in Experiments with the Mini-Robot Khepera - Proceedings of the 1st International Khepera Workshop, Löffler, A., Mondada, F., and Rückert, U. (eds.), pp. 31 - 40. Paderborn, Germany. HNI. 1999.

[.ps.Z]

Niklasson, L. and Bodén, M. Representing Structure and Structured Representations in Connectionist Networks, in Current Perspectives in Neural Computing, Browne (ed.), IOP. 1997.

Ziemke, T., Bodén, M., and Niklasson, L. Oil Spill Detection: A Case Study of Recurrent Artificial Neural Networks, in Current Perspectives in Neural Computing, Browne (ed.), IOP. 1997.

[.ps.Z]

Bodén, M. A Connectionist Variation on Inheritance, in Artificial Neural Networks - ICANN 96, Bochum, Springer. 1996.

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Narayanan, A. and Bodén, M. Representations for Changing One's Mind, Forms of Representations, Peterson (ed.), Intellect. 1996.

Bodén, M. and Niklasson, L. Features of Distributed Representations for Tree-structures: A Study of RAAM, in Current Trends in Connectionism - Proceedings of the 1995 Swedish Conference on Connectionism, Skövde, Lawrence Erlbaum. 1995.

[.ps.Z]

Bodén, M. On Biased Learning for Generalisation, in Proceedings of the International Conference on Neural Information Processing, Seoul, 1994.

Bodén, M. and Narayanan, A. A Representational Architecture for Nonmonotonic Inheritance Structures, in Proceedings of the International Conference on Artificial Neural Networks - 1993, Amsterdam, Springer Verlag. 1993.

Bodén, M. and Narayanan, A. A Connectionist Model of Nonmonotonic Reasoning: Handling Exceptions in Inheritance Hierarchies, in Connectionism in a Broad Perspective - Selected Papers from the First Swedish Conference on Connectionism - 1992, Skövde, Ellis Horwood. 1992.

Books/special issues

Bodén, M. and Bailey, T. L., Eds. Intelligent Systems for Bioinformatics 2006. Proceedings of the 2006 Workshop on Intelligent Systems for Bioinformatics (WISB 2006). CRPIT. 73. Hobart, Australia, ACS.

Online

Proceedings of the International Conference on Artificial Neural Networks -- 1998, Niklasson, Bodén and Ziemke (eds.), Springer, 1998.

Current Trends in Connectionism, Niklasson and Bodén (eds), Lawrence Erlbaum, 1995.

Connectionism in a Broad Perspective: Selected Papers from the Swedish Conference on Connectionism - 1992, Niklasson and Bodén (eds.), Ellis Horwood, 1994.

Special Issue on Connectionism, Bodén and Niklasson (eds.), Artificial Intelligence Review, Vol. 7, No. 5, Kluwer Academic Publishers, 1993.

Thesis and miscellaneous

Bodén, M. Context-sensitive connectionist representations for nonmonotonic inheritance, Unpublished PhD thesis, University of Exeter, UK. 1997.

Bodén, M. A guide to recurrent neural networks and backpropagation, in The DALLAS project. Report from the NUTEK-supported project AIS-8: Application of Data Analysis with Learning Systems, 1999-2001. Holst, A. (ed.), SICS Technical Report T2002:03, SICS, Kista, Sweden. 2002.

Bodén, M. Predicting protein localization using recurrent neural networks, Poster presented at Intelligent Systems and Molecular Biology (ISMB), Brisbane. 2003.

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