Professor Mikael Boden
Professor

Mikael Boden

Email: 
Phone: 
+61 7 336 51307

Background

Mikael Bodén has a PhD in Computer Science and statistical machine learning from the University of Exeter (UK) but has spent the last decade and a half in biological research environments, including the Institute for Molecular Bioscience/ARC Centre of Excellence in Bioinformatics and the School of Chemistry and Molecular Biosciences, where he is currently located. He is the director of UQ’s postgraduate program in bioinformatics. Mikael Bodén has supervised 7 postdocs from funding he received from both ARC and NHMRC; he has been the primary advisor for 11 PhD and 3 MPhil graduates; he is currently supervising another 6 PhD students in bioinformatics and computational biology. Mikael Bodén collaborates with researchers in neuroscience, developmental biology, protein engineering and bioeconomy to mention but a few, and contributes expertise in the processing, analysis and integration of biological data; this is exemplified by recent publications in Science, Nature Catalysis, Nature Communications, Cell Systems, Nucleic Acids Research and Bioinformatics.

Availability

Professor Mikael Boden is:
Available for supervision
Media expert

Fields of research

Bioinformatics and computational biology Biological Sciences Genetics Genomics Information and Computing Sciences Modelling and simulation Systems biology

Qualifications

  • Bachelor of Science, Skövde University College
  • Masters (Coursework) of Science, Skövde University College
  • Doctor of Philosophy, University of Exeter

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

Search Professor Mikael Boden’s works on UQ eSpace

129 works between 1993 and 2024
All (129) Journal Article (93) Book Chapter (4) Conference Publication (31) Edited Outputs (1)

81 - 100 of 129 works

2008

Journal Article

Network: Computation in Neural Systems: Editorial

Goodhill, Geoff, Baker, Curtis, Balasubramanian, Vijay, Bazhenov, Maxim, Beck, Jeffrey, Becker, Sue, Bethge, Matthias, Boahen, Kwabena, Boden, Mikael, Bonin, Vincent, Bouret, Sebastien, Fairhall, Adrienne, Flash, Tamar, French, Robert, Gillies, Andrew, Gollisch, Tim, Gurney, Kevin, Gutkin, Boris, Hayhoe, Mary, Hunt, Jonathan, Ibbotson, Michael, Kepecs, Adam, Kingdom, Fred, Kropff, Emilio, Longden, Kit, Marder, Eve, Miikkulainen, Risto, Oliva, Aude, Olshausen, Bruno ... Zhaoping, Li (2008). Network: Computation in Neural Systems: Editorial. Network: Computation in Neural Systems, 19 (1), 1-2. doi: 10.1080/09548980801915409

Network: Computation in Neural Systems: Editorial

2008

Conference Publication

Predicting SUMOylation sites

Bauer, Denis C., Buske, Fabian A. and Boden, Mikael (2008). Predicting SUMOylation sites. 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, Melbourne, Australia, 15-17 October, 2008. Berlin, Germany: Springer-Verlag Berlin. doi: 10.1007/978-3-540-88436-1-3

Predicting SUMOylation sites

2007

Journal Article

Computing the reversal distance between genomes in the presence of multi-gene families via binary integer programming

Suksawatchon, J., Lursinsap, C. and Boden, M. (2007). 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. doi: 10.1142/S0219720007002552

Computing the reversal distance between genomes in the presence of multi-gene families via binary integer programming

2007

Journal Article

Evolving spelling exercises to suit individual student needs

Boden, Marie and Boden, Mikael (2007). Evolving spelling exercises to suit individual student needs. Applied Soft Computing, 7 (1), 126-135. doi: 10.1016/j.asoc.2005.03.001

Evolving spelling exercises to suit individual student needs

2007

Journal Article

Predicting Nuclear Localization

Hawkins, John, Davis, Lynne and Boden, Mikael (2007). Predicting Nuclear Localization. Journal of Proteome Research, 6 (4), 1402-1409. doi: 10.1021/pr060564n

Predicting Nuclear Localization

2007

Book Chapter

Markovian bias of neural-based architectures with feedback connections

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

Markovian bias of neural-based architectures with feedback connections

2007

Conference Publication

Predicting nucleolar proteins using support-vector machines

Bodén, Mikael (2007). Predicting nucleolar proteins using support-vector machines. 6th Asia-Pacific Bioinformatics Conference, Kyoto, Japan, 14-17 January, 2008. London, U.K.: Imperial College Press. doi: 10.1142/9781848161092_0005

Predicting nucleolar proteins using support-vector machines

2007

Conference Publication

A comparison of sequence kernels for localization prediction of transmembrane proteins

Maetschke, S., Gallagher, M. and Boden, M. (2007). A comparison of sequence kernels for localization prediction of transmembrane proteins. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology 2007 (CIBCB 2007), Honolulu, Hawaii, 1-5 April 2007. Piscataway, NJ, U.S.A.: IEEE - Institute of Electrical Electronics Engineers Inc.. doi: 10.1109/cibcb.2007.4221246

A comparison of sequence kernels for localization prediction of transmembrane proteins

2007

Conference Publication

Understanding prediction systems for HLA-binding peptides and t-cell epitope identification

You, L., Zhang, P., Boden, M. and Brusic, V. (2007). Understanding prediction systems for HLA-binding peptides and t-cell epitope identification. Pattern Recognition in Bioinformatics (PRIB 2007), Singapore, 1-2 October 2007. Heidelberg, Germany: Springer. doi: 10.1007/978-3-540-75286-8_32

Understanding prediction systems for HLA-binding peptides and t-cell epitope identification

2007

Journal Article

Identifying novel peroxisomal proteins

Hawkins, J., Mahony, D., Maetschke, S., Wakabayashi, M., Teasdale, R. D. and Boden, M. (2007). Identifying novel peroxisomal proteins. Proteins: Structure Function and Bioinformatics, 69 (3), 606-616. doi: 10.1002/prot.21420

Identifying novel peroxisomal proteins

2007

Conference Publication

Decoupling signal recognition from sequence models of protein secretion

Buske, Fabian and Boden, Mikael (2007). Decoupling signal recognition from sequence models of protein secretion. Computational Models for Life Sciences - CMLS'07, Gold Coast, QLD, Australia, 17 - 19 December 2007. Melville, NY, U.S.A.: American Institute of Physics. doi: 10.1063/1.2816618

Decoupling signal recognition from sequence models of protein secretion

2007

Conference Publication

Reducing the number of support vectors to allay inefficiency of large-scale models in computational biology

Dufton, L. and Boden, M. (2007). Reducing the number of support vectors to allay inefficiency of large-scale models in computational biology. Computational Models for Life Sciences - CMLS'07, Queensland, Australia, 17-19 December, 2007. New York: American Institute of Physics. doi: 10.1063/1.2816639

Reducing the number of support vectors to allay inefficiency of large-scale models in computational biology

2006

Journal Article

Detecting and sorting targeting peptides wtih neural networks and support vector machines

Hawkins, John and Boden, Mikael (2006). Detecting and sorting targeting peptides wtih neural networks and support vector machines. Journal of Bioinformatics and Computational Biology, 4 (1), 1-18. doi: 10.1142/S0219720006001771

Detecting and sorting targeting peptides wtih neural networks and support vector machines

2006

Conference Publication

Evolving discriminative motifs for recognizing proteins imported to the peroxisome via the PTS2 pathway

Boden, M. B. and Hawkins, J. C. (2006). Evolving discriminative motifs for recognizing proteins imported to the peroxisome via the PTS2 pathway. 2006 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, 16-21 July, 2006. Piscataway, N.J., USA: IEEE. doi: 10.1109/cec.2006.1688653

Evolving discriminative motifs for recognizing proteins imported to the peroxisome via the PTS2 pathway

2006

Journal Article

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

Boden, M., Yuan, Z. and Bailey, T. L. (2006). Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures. Bmc Bioinformatics, 7 (68) 68, 1-12. doi: 10.1186/1471-2105-7-68

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

2006

Journal Article

Identifying sequence regions undergoing conformational change via predicted continuum secondary structure

Boden, M. and Bailey, T. L. (2006). Identifying sequence regions undergoing conformational change via predicted continuum secondary structure. Bioinformatics, 22 (15), 1809-1814. doi: 10.1093/bioinformatics/btl198

Identifying sequence regions undergoing conformational change via predicted continuum secondary structure

2006

Edited Outputs

Proceedings of the A1 2006 Workshop on Intelligent Systems of Bioinformatics (WISB 2006)

Mikael Boden and Timothy L. Bailey eds. (2006). Proceedings of the A1 2006 Workshop on Intelligent Systems of Bioinformatics (WISB 2006). Intelligent Systems for Bioinformatics 2006, Hobart, Australia, 4 December, 2006. New South Wales, Australia: Australian Computer Society Inc..

Proceedings of the A1 2006 Workshop on Intelligent Systems of Bioinformatics (WISB 2006)

2006

Conference Publication

Comparing SVM sequence kernels: A protein subcellular localization theme

Davis, L., Hawkins, J. C., Maetschke, S. R. and Boden, M B (2006). Comparing SVM sequence kernels: A protein subcellular localization theme. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB 2006), Hobart, Tas, Australia, 4 December, 2006. New South Wales, Australia: Australian Computer Society Inc..

Comparing SVM sequence kernels: A protein subcellular localization theme

2006

Conference Publication

Higher order HMMs for localization prediction of transmembrance proteins

Maetschke, S. R., Boden, M B and Gallagher, M R (2006). Higher order HMMs for localization prediction of transmembrance proteins. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB 2006), Hobart, Australia, 4 December, 2006. New South Wales, Australia: Australian Computer Society Inc..

Higher order HMMs for localization prediction of transmembrance proteins

2006

Journal Article

Predicting the solvent accessibility of transmembrane residues from protein sequence

Yuan, Z., Zhang, F. S., Davis, M. J., Boden, M. and Teasdale, R. D. (2006). Predicting the solvent accessibility of transmembrane residues from protein sequence. Journal of Proteome Research, 5 (5), 1063-1070. doi: 10.1021/pr050397b

Predicting the solvent accessibility of transmembrane residues from protein sequence

Current funding

  • 2023 - 2026
    What drives the Anterior Expansion of the Central Nervous System?
    ARC Discovery Projects
    Open grant
  • 2023 - 2024
    What is the common factor driving brain overgrowth in ASD? Investigating the relationship between epigenetic marks neural stem cell proliferation.
    Simons Foundation Autism Research Initiative - Pilot Award
    Open grant
  • 2022 - 2026
    TRIAGE: A disease agnostic computational and modelling platform to accelerate variant classification
    NHMRC MRFF Genomics Health Futures Mission
    Open grant
  • 2022 - 2024
    Dual-function ribonucleases: unexpected agents of antibiotic resistance
    NHMRC IDEAS Grants
    Open grant
  • 2021 - 2024
    EnzOnomy - an enzyme-based production pipeline for the bioeconomy
    ARC Discovery Projects
    Open grant

Past funding

  • 2016 - 2019
    Reconstructing proteins to explain and engineer biological diversity
    ARC Discovery Projects
    Open grant
  • 2012 - 2015
    Tracing nature's template: Using statistical machine learning to evolve biocatalysts
    ARC Discovery Projects
    Open grant
  • 2011
    A systems biology approach to elucidate common principles and mechanisms underlying triplet repeat expansion associated genetic defects
    NHMRC Project Grant
    Open grant
  • 2005
    Mining Long-range Dependencies and Interactions in Amino Acid Sequences
    UQ Early Career Researcher
    Open grant
  • 2003 - 2005
    Recurrent neural networks for biological sequence analysis
    UQ New Staff Research Start-Up Fund
    Open grant

Availability

Professor Mikael Boden is:
Available for supervision

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Supervision history

Current supervision

  • Doctor Philosophy

    Do cell types exist in a continuum? Single-cell bioinformatics across species, over time and space

    Principal Advisor

    Other advisors: Professor Stefan Thor

  • Doctor Philosophy

    Finding meaningful variation in biological data by deep learning

    Principal Advisor

    Other advisors: Dr Gabriel Foley

  • Doctor Philosophy

    Merger of natural and engineered biological sequence space

    Principal Advisor

    Other advisors: Dr Michael Forbes

  • Doctor Philosophy

    Dual-function ribonucleases: unexpected agents of antibiotic resistance

    Principal Advisor

    Other advisors: Professor Phil Hugenholtz, Dr Gabriel Foley

  • Doctor Philosophy

    Using Statistical Models to Integrate Epigenetic Information by Distinguishing Sources of Variability

    Principal Advisor

    Other advisors: Professor Michael Piper, Dr Gabriel Foley

  • Doctor Philosophy

    Bioinformatics of epigenetics at multiple scales: from evolution of epigenetic factors to tracing their marks in organellar development

    Principal Advisor

    Other advisors: Professor Stefan Thor

  • Doctor Philosophy

    Decoding the genetic pathways governing cell diversity in the mammalian hypothalamus

    Associate Advisor

    Other advisors: Professor Michael Piper, Professor Stefan Thor

  • Doctor Philosophy

    How SETD2 shapes cortical development

    Associate Advisor

    Other advisors: Professor Stefan Thor, Professor Michael Piper

  • Doctor Philosophy

    Changing the prokaryotic classification status quo with a global genome-based taxonomy

    Associate Advisor

    Other advisors: Professor Phil Hugenholtz

Completed supervision

Enquiries

Contact Professor Mikael Boden directly for media enquiries about:

  • Artificial Intelligence (AI)
  • Bioinformatics
  • Biology and computers
  • Computational biology
  • Computer learning
  • DNA sequencing
  • Machine learning
  • Systems biology

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