Associate Professor Mikael Boden

Associate Professor

School of Chemistry and Molecular Biosciences
Faculty of Science

Affiliate Research Fellow

Institute for Molecular Bioscience
m.boden@uq.edu.au
+61 7 336 51307

Overview

Researcher in bioinformatics.

Mikael Boden is a computer scientist with interests in computational biology, bioinformatics, and statistical machine learning. He received a PhD in computer science from the University of Exeter, UK.

Research Interests

  • Bioinformatics
    Thanks to major advances in biotechnology and instrumentation, biology is becoming an information centred science. The field of bioinformatics draws on computer science, math and statistics to enable discoveries in biological data sets. Our research aims to develop, investigate and apply bioinformatics methodologies to understand and resolve a range of open problems in genomics, molecular and systems biology. Recent applications involve protein sorting, nuclear protein organisation, mechanisms of transcriptional regulation, sequence and structure determinants of protein function and modification, and protein engineering. Biological data are now available at scales that challenges our ability to process and analyse them. On the flip side, greater scale gives statistical power to distinguish biologically meaningful signals from mere noise or artefacts, i.e. to identify "drivers" and "determinants" of function and structure. Sometimes the number of features (that describe each observation) is so great that we must use (biological) expertise to constrain the search for signals. Broadly put, our research aims to 1. effectively manage the complexity of operations involved in analysing millions of sequence reads, thousands of genomes, and proteomes of thousands of dynamically regulated molecules, etc 2. enable the seamless aggregation (or integration) of uncertain and incomplete data, typical of the next wave of biotechnology, across genomics, proteomics, structural biology, etc, and of using biological expertise 3. empower the interpretation of "whole system" data, aimed at understanding of basis of disease and other scientifically relevant phenotypes, using statistics and machine learning

Qualifications

  • Bachelor of Science (Computer Science), Skövde University College
  • Master of Science (Computer Science), Skövde University College
  • PhD (Computer Science), University of Exeter

Publications

View all Publications

Supervision

  • Doctor Philosophy

  • Master Philosophy

  • (2017) Doctor Philosophy

View all Supervision

Publications

Book Chapter

  • Zaugg, Julian, Gumulya, Yosephine, Gillam, Elizabeth M. J. and Bodén, Mikael (2014). Computational tools for directed evolution: a comparison of prospective and retrospective strategies. In Elizabeth M. J. Gillam, Janine N. Copp and David F. Ackerley (Ed.), Directed evolution library creation: methods and protocols 2nd ed. (pp. 315-333) New York, NY, United States: Humana Press. doi:10.1007/978-1-4939-1053-3_21

  • Tino, P., Hammer, B. and Boden, M. (2007). Markovian bias of neural-based architectures with feedback connections. In Hammer, B. and Hitzler, P. (Ed.), Perspectives of Neural-Symbolic Integration (pp. 95-133) Heidelberg, Germany: Springer-Verlag. doi:10.1007/978-3-540-73954-8_5

  • Wiles, J. H., Blair, A. D. and Boden, M. B. (2001). Representation beyond finite states: Alternatives to pushdown automata. In J.J. Kolen and S.C. Kremer (Ed.), A Field Guide to Dynamical Recurrent Networks 1 ed. (pp. 129-142) Piscataway, New Jersey, U.S.A.: IEEE.

Journal Article

Conference Publication

  • Bauer, Denis C., Willadsen, Kai, Buske, Fabian A., Le Cao, Kim-Anh, Bailey, Timothy L., Dellaire, Graham and Boden, Mikael (2011). Sorting the nuclear proteome. In: 19th Annual International Conference on Intelligent Systems for Molecular Biology/10th European Conference on Computational Biology, Vienna Austria, (I7-I14). Jul 17-19, 2011. doi:10.1093/bioinformatics/btr217

  • Mazgut, Jakub, Tino, Peter, Boden, Mikael and Yan, Hong (2010). Multilinear decomposition and topographic mapping of binary tensors. In: Konstantinos Diamantaras, Wlodek Duch and Lazaros S. Iliadis, Artificial Neural Networks – ICANN 2010: Proceedings of the 20th International Conference, Part I. 20th International Conference on Artificial Neural Networks (ICANN 2010), Thessaloniki, Greece, (317-326). 15-18 September 2010. doi:10.1007/978-3-642-15819-3_42

  • Arieshanti, I., Boden, M., Maetschke, S and Buske, F. A. (2009). Detecting sequence and structure homology via an integrative kernel: A case-study in recognizing enzymes. In: Proceedings of the 2009 IEEE Computational Intelligence for Bioinformatics and Computational Biology. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology 2009, Nashville, Tennessee, U.S.A., (46-52). 30 March - 2 April , 2009. doi:10.1109/CIBCB.2009.4925706

  • Bauer, Denis C., Buske, Fabian A. and Boden, Mikael (2008). Predicting SUMOylation sites. In: Chetty, M., Ngom, A. and Ahmad, S., Pattern Recognition in Bioinformatics, Proceedings. 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, Melbourne, Australia, (28-40). 15-17 October, 2008.

  • Maetschke, S., Gallagher, M. and Boden, M. (2007). A comparison of sequence kernels for localization prediction of transmembrane proteins. In: D. Fogel, Computational Intelligence in Bioinformatics and Computational Biology 2007 (CIBCB 2007). IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology 2007 (CIBCB 2007), Honolulu, Hawaii, (367-372). 1-5 April 2007.

  • Buske, Fabian and Boden, Mikael (2007). Decoupling signal recognition from sequence models of protein secretion. In: Computational Models for Life Sciences - CMLS'07. Computational Models for Life Sciences - CMLS'07, Gold Coast, QLD, Australia, (147-156). 17 - 19 December 2007. doi:10.1063/1.2816618

  • Bodén, Mikael (2007). Predicting nucleolar proteins using support-vector machines. In: Alvis Brazma, Satoru Miyano and Tatsuya Akutsu, Proceedings of the 6th Asia-Pacific Bioinformatics Conference. 6th Asia-Pacific Bioinformatics Conference, Kyoto, Japan, (19-28). 14-17 January, 2008. doi:10.1142/9781848161092_0005

  • Dufton, L. and Boden, M. (2007). Reducing the number of support vectors to allay inefficiency of large-scale models in computational biology. In: Pham, T.D. and Zhou, X., Proceedings of the 2007 International Symposium on Computational Models for Life Sciences (CMLS'07). Computational Models for Life Sciences - CMLS'07, Queensland, Australia, (340-348). 17-19 December, 2007. doi:10.1063/1.2816639

  • You, L., Zhang, P., Boden, M. and Brusic, V. (2007). Understanding prediction systems for HLA-binding peptides and t-cell epitope identification. In: Lecture Notes in bioinformatics. Pattern Recognition in Bioinformatics (PRIB 2007), Singapore, (337-348). 1-2 October 2007.

  • Davis, L., Hawkins, J. C., Maetschke, S. R. and Boden, M B (2006). Comparing SVM sequence kernels: A protein subcellular localization theme. In: M. Boden and T. Bailey, Proceedings of the 2006 Workshop on Intelligent Systems for Bioinformatics (WISB 2006). 2006 Workshop on Intelligent Systems for Bioinformatics (WISB 2006), Hobart, Tas, Australia, (39-47). 4 December, 2006.

  • Boden, M. B. and Hawkins, J. C. (2006). Evolving discriminative motifs for recognizing proteins imported to the peroxisome via the PTS2 pathway. In: G.G Yen, 2006 IEEE Congress on Evolutionary Computation. 2006 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, (2750-2755). 16-21 July, 2006.

  • Maetschke, S. R., Boden, M B and Gallagher, M R (2006). Higher order HMMs for localization prediction of transmembrance proteins. In: M. Boden and T. Bailey, Proceedings of the 2006 Workshop on Intelligent Systems for Bioinformatics (WISB 2006). 2006 Workshop on Intelligent Systems for Bioinformatics (WISB 2006), Hobart, Australia, (49-53). 4 December, 2006.

  • Hawkins, J. and Boden, M. (2006). Multi-stage redundancy reduction: effective utilisation of small protein data sets. In: Mikael Boden and Timothy L. Bailey, Proceedings of the A1 2006 Workshop on Intelligent Systems of Bioinformatics (WISB 2006). Intelligent Systems for Bioinformatics 2006, Hobart, Australia, (55-59). 4 December, 2006.

  • Maetschke, S. R., Towsey, M. and Boden, M. B. (2005). BLOMAP: An encoding of amino acids which improves signal peptide cleavage site prediction. In: Y.P. Phoebe Chen and L. Wong, Proceedings of the 3rd Asia Pacific Bioinformatics Conference. 3rd Asia Pacific Bioinformatics Conference, Singapore, (141-150). 17-21 January 2005.

  • Boden, M. B. and Hawkins, J. C. (2005). Detecting residues in targeting peptides. In: Y. P. Chen and L. Wong, Proceedings of the 3rd Asia-Pacific Bioinformatics Conference. 3rd Asia-Pacific Bioinformatics Conference, Singapore, (131-140). 17-21 January, 2005.

  • Wakabayashi, M., Hawkins, J. C., Maetschke, S. R. and Boden, M. B. (2005). Exploiting sequence dependencies in the prediction of peroxisomal proteins. In: M. Gallagher, J. Hogan and F. Maire, Intelligent Data Engineering and Automated Learning - IDEAL2005. Intelligent Data Engineering and Automated Learning - IDEAL2005, Brisbane, Australia, (454-461). 6-8 July 2005.

  • Suksawatchon, J., Lursinsap, C. and Boden, M. B. (2005). Heuristic algorithm for computing reversal distance with multigene families via binary integer programming. In: G. B. Fogel, Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2005 (CIBCB '05). IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, U.S.A., (187-193). 14-15 November 2005.

  • Hawkins, J. C. and Boden, M. B. (2005). Predicting peroxisomal proteins. In: G. B. Fogel, Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, USA, (469-474). 14-15 November, 2005.

  • Bauer, Denis C., Boden, Mikael, Thier, Ricarda and Yuan, Zheng (2005). Predicting structural disruption of proteins caused by crossover. In: G. B. Fogel, Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2005 (CIBCB '05). IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, U.S.A., (514-520). 14-15 November 2005.

  • Boden, M. B. (2003). Using evolutionary noise to improve prediction of rapidly evolving targeting peptides. In: R. Sarker, R. Reynolds and H. Abbass et al., 2003 Congress on Evolutionary Computation. 2003 Congress on Evolutionary Computation, Canberra, (2821-2828). 8-12 December, 2003. doi:10.1109/CEC.2003.1299446

  • Ziemke, T., Carlsson, J. and Boden, M. (1999). An experimental comparison of weight evolution in neural control architectures for a 'Garbage-Collecting' Khepera robot. In: A. Loffler, F. Mondada and U. Ruckert, Proceedings of the 1st International Khepera Workshop. 1st International Khepera Workshop, Paderborn, Germany, (31-40). 10-11 December 1999.

  • Niklasson, L. and Boden, M. B. (1999). Content, context and connectionist networks. In: Proceedings of the 21st Annual Meeting of the Cognitive Science Society. 21st Annual Meeting of the Cognitive Science Society, Vancouver, (474-479). August 1999.

  • Boden, M. B., Wiles, J. H., Tonkes, B. and Blair, A. D. (1999). Learning to predict a context-free language: Analysis of dynamics in recurrent hidden units. In: Proceedings of the Ninth International Conference on Artificial Neural Networks. ICANN'99, Edinburgh, UK, (359-364). 7-10 September, 1999.

  • Boden, M, Wiles, J, Tonkes, B and Blair, A (1999). Learning to predict a context-free language: Analysis of dynamics in recurrent hidden units. In: Ninth International Conference On Artificial Neural Networks (icann99), Vols 1 and 2. 9th International Conference on Artificial Neural Networks (ICANN99), Edinburgh Scotland, (359-364). Sep 07-10, 1999.

  • Boden, M. B., Wiles, J. H., Tonkes, B. and Blair, A. D. (1999). On the ability of recurrent nets to learn deeply embedded structures. In: CL Giles and R Sun, Proceedings of the Sixteenth International Joint conference on Artificial Intelligence. IJCAI'99, Stockholm, (67-72). 31 July - 6 August 1999.

  • Boden, M and Niklasson, L (1995). Features of distributed representations for tree-structures: A study of RAAM. In: Current Trends in Connectionism. Swedish Conference on Connectionism, Skovde Sweden, (121-139). Mar 02-03, 1995.

Edited Outputs

PhD and MPhil Supervision

Current Supervision

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Master Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

    Other advisors:

Completed Supervision