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

134 works between 1993 and 2025
All (134) Journal Article (97) Book Chapter (4) Conference Publication (32) Edited Outputs (1)

101 - 120 of 134 works

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

2006

Conference Publication

Multi-stage redundancy reduction: effective utilisation of small protein data sets

Hawkins, J. and Boden, M. (2006). Multi-stage redundancy reduction: effective utilisation of small protein data sets. Intelligent Systems for Bioinformatics 2006, Hobart, Australia, 4 December, 2006. New South Wales, Australia: Australian Computer Society Inc..

Multi-stage redundancy reduction: effective utilisation of small protein data sets

2006

Journal Article

STAR: predicting recombination sites from amino acid sequence

Bauer, Denis C., Boden, Mikael, Thier, Ricarda and Gillam, Elizabeth M. (2006). STAR: predicting recombination sites from amino acid sequence. BMC Bioinformatics, 7 (437) 437, 437. doi: 10.1186/1471-2105-7-437

STAR: predicting recombination sites from amino acid sequence

2005

Conference Publication

Heuristic algorithm for computing reversal distance with multigene families via binary integer programming

Suksawatchon, J., Lursinsap, C. and Bodén, M. (2005). Heuristic algorithm for computing reversal distance with multigene families via binary integer programming. IEEE Computer Society. doi: 10.1109/cibcb.2005.1594916

Heuristic algorithm for computing reversal distance with multigene families via binary integer programming

2005

Conference Publication

Predicting structural disruption of proteins caused by crossover

Bauer, Denis C., Bodén, Mikael, Thier, Ricarda and Yuan, Zheng (2005). Predicting structural disruption of proteins caused by crossover. IEEE Computer Society. doi: 10.1109/cibcb.2005.1594962

Predicting structural disruption of proteins caused by crossover

2005

Conference Publication

Predicting peroxisomal proteins

Hawkins, John and Bodén, Mikael (2005). Predicting peroxisomal proteins. 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB '05, , , November 14, 2005-November 15, 2005. IEEE Computer Society. doi: 10.1109/cibcb.2005.1594956

Predicting peroxisomal proteins

2005

Journal Article

Prediction of subcellular localization using sequence-biased recurrent networks

Boden, Mikael and Hawkins, John (2005). Prediction of subcellular localization using sequence-biased recurrent networks. Bioinformatics, 21 (10), 2279-2286. doi: 10.1093/bioinformatics/bti372

Prediction of subcellular localization using sequence-biased recurrent networks

2005

Conference Publication

Detecting residues in targeting peptides

Boden, M. B. and Hawkins, J. C. (2005). Detecting residues in targeting peptides. 3rd Asia-Pacific Bioinformatics Conference, Singapore, 17-21 January, 2005. London, United Kingdom: Imperial College Press. doi: 10.1142/9781860947322_0013

Detecting residues in targeting peptides

2005

Conference Publication

Heuristic algorithm for computing reversal distance with multigene families via binary integer programming

Suksawatchon, J., Lursinsap, C. and Boden, M. B. (2005). Heuristic algorithm for computing reversal distance with multigene families via binary integer programming. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, U.S.A., 14-15 November 2005. Piscataway, NJ, U.S.A.: IEEE Press.

Heuristic algorithm for computing reversal distance with multigene families via binary integer programming

2005

Conference Publication

Exploiting sequence dependencies in the prediction of peroxisomal proteins

Wakabayashi, M., Hawkins, J. C., Maetschke, S. R. and Boden, M. B. (2005). Exploiting sequence dependencies in the prediction of peroxisomal proteins. Intelligent Data Engineering and Automated Learning - IDEAL2005, Brisbane, Australia, 6-8 July 2005. Berlin, Germany: Springer-Verlag. doi: 10.1007/11508069_59

Exploiting sequence dependencies in the prediction of peroxisomal proteins

2005

Conference Publication

Predicting peroxisomal proteins

Hawkins, J. C. and Boden, M. B. (2005). Predicting peroxisomal proteins. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, USA, 14-15 November, 2005. Piscataway, NJ, USA: IEEE Press.

Predicting peroxisomal proteins

2005

Journal Article

Improved Access To Sequential Motifs: A Note On The Architectural Bias Of Recurrent Networks

Boden, Mikael and Hawkins, John (2005). Improved Access To Sequential Motifs: A Note On The Architectural Bias Of Recurrent Networks. IEEE Transactions on Neural Networks, 16 (2), 491-494. doi: 10.1109/TNN.2005.844086

Improved Access To Sequential Motifs: A Note On The Architectural Bias Of Recurrent Networks

2005

Conference Publication

Predicting structural disruption of proteins caused by crossover

Bauer, Denis C., Boden, Mikael, Thier, Ricarda and Yuan, Zheng (2005). Predicting structural disruption of proteins caused by crossover. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San Diego, U.S.A., 14-15 November 2005. Piscataway, NJ, U.S.A.: IEEE Press.

Predicting structural disruption of proteins caused by crossover

2005

Conference Publication

BLOMAP: An encoding of amino acids which improves signal peptide cleavage site prediction

Maetschke, S. R., Towsey, M. and Boden, M. B. (2005). BLOMAP: An encoding of amino acids which improves signal peptide cleavage site prediction. 3rd Asia Pacific Bioinformatics Conference, Singapore, 17-21 January 2005. London, UK: Imperial College Press. doi: 10.1142/9781860947322_0014

BLOMAP: An encoding of amino acids which improves signal peptide cleavage site prediction

2005

Journal Article

The applicability of recurrent neural networks for biological sequence analysis

Hawkins, J. and Boden, M. (2005). The applicability of recurrent neural networks for biological sequence analysis. IEEE-ACM Transactions on Computational Biology and Bioinformatiocs, 2 (3), 243-253. doi: 10.1109/TCBB.2005.44

The applicability of recurrent neural networks for biological sequence analysis

2004

Journal Article

Generalization by symbolic abstraction in cascaded recurrent networks

Boden, Mikael (2004). Generalization by symbolic abstraction in cascaded recurrent networks. Neurocomputing, 57 (1-4), 87-104. doi: 10.1016/j.neucom.2004.01.006

Generalization by symbolic abstraction in cascaded recurrent networks

Current funding

  • 2025 - 2028
    Learning from the past to design drugs for the future
    NHMRC IDEAS Grants
    Open grant
  • 2023 - 2026
    What drives the Anterior Expansion of the Central Nervous System?
    ARC Discovery Projects
    Open grant
  • 2023 - 2025
    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 - 2025
    Dual-function ribonucleases: unexpected agents of antibiotic resistance
    NHMRC IDEAS Grants
    Open grant

Past funding

  • 2021 - 2024
    EnzOnomy - an enzyme-based production pipeline for the bioeconomy
    ARC Discovery Projects
    Open grant
  • 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

    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

  • 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

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

    Principal Advisor

    Other advisors: Professor Michael Piper

  • 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

    Grounding the computational design of enzymes in their relative diversity

    Principal Advisor

    Other advisors: Professor Gary Schenk

  • 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: Professor Nathan Palpant

  • Doctor Philosophy

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

    Associate Advisor

    Other advisors: Professor Phil Hugenholtz

  • Doctor Philosophy

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

    Associate Advisor

    Other advisors: Professor Phil Hugenholtz

  • 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

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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