John Hawkins

Summary

Affiliation: University of Queensland
Country: Australia

Publications

  1. ncbi request reprint Detecting and sorting targeting peptides with neural networks and support vector machines
    John Hawkins
    School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Australia
    J Bioinform Comput Biol 4:1-18. 2006
  2. ncbi request reprint The applicability of recurrent neural networks for biological sequence analysis
    John Hawkins
    School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Australia
    IEEE/ACM Trans Comput Biol Bioinform 2:243-53. 2005
  3. ncbi request reprint Predicting nuclear localization
    John Hawkins
    ARC Centre for Complex Systems, School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Australia
    J Proteome Res 6:1402-9. 2007
  4. ncbi request reprint Identifying novel peroxisomal proteins
    John Hawkins
    ARC Centre for Complex Systems, The University of Queensland, St Lucia, Queensland 4072, Australia
    Proteins 69:606-16. 2007
  5. ncbi request reprint Prediction of subcellular localization using sequence-biased recurrent networks
    Mikael Boden
    School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Australia
    Bioinformatics 21:2279-86. 2005
  6. pmc Assessing phylogenetic motif models for predicting transcription factor binding sites
    John Hawkins
    Institute for Molecular Bioscience, University of Queensland, QLD, Australia
    Bioinformatics 25:i339-47. 2009
  7. ncbi request reprint Improved access to sequential motifs: a note on the architectural bias of recurrent networks
    Mikael Boden
    IEEE Trans Neural Netw 16:491-4. 2005

Detail Information

Publications7

  1. ncbi request reprint Detecting and sorting targeting peptides with neural networks and support vector machines
    John Hawkins
    School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Australia
    J Bioinform Comput Biol 4:1-18. 2006
    ..873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP)...
  2. ncbi request reprint The applicability of recurrent neural networks for biological sequence analysis
    John Hawkins
    School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Australia
    IEEE/ACM Trans Comput Biol Bioinform 2:243-53. 2005
    ..Furthermore, as the patterns within the sequence become more ambiguous, the choice of specific recurrent architecture becomes more critical...
  3. ncbi request reprint Predicting nuclear localization
    John Hawkins
    ARC Centre for Complex Systems, School of Information Technology and Electrical Engineering, University of Queensland, QLD 4072, Australia
    J Proteome Res 6:1402-9. 2007
    ..The final predictor NUCLEO operates with a realistic success rate of 0.70 and a correlation coefficient of 0.38, as established on the independent test set. (NUCLEO is available at: http://pprowler.itee.uq.edu.au.)...
  4. ncbi request reprint Identifying novel peroxisomal proteins
    John Hawkins
    ARC Centre for Complex Systems, The University of Queensland, St Lucia, Queensland 4072, Australia
    Proteins 69:606-16. 2007
    ..We use the predictor to screen several additional eukaryotic genomes to revise previously estimated numbers of peroxisomal proteins. Available at http://pprowler.itee.uq.edu.au...
  5. ncbi request reprint Prediction of subcellular localization using sequence-biased recurrent networks
    Mikael Boden
    School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Australia
    Bioinformatics 21:2279-86. 2005
    ..However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging...
  6. pmc Assessing phylogenetic motif models for predicting transcription factor binding sites
    John Hawkins
    Institute for Molecular Bioscience, University of Queensland, QLD, Australia
    Bioinformatics 25:i339-47. 2009
    ..However, these phylogenetic motif models (PMMs) have never been rigorously benchmarked in order to determine whether they lead to better prediction of TFBSs than obtained using simple position weight matrix scanning...
  7. ncbi request reprint Improved access to sequential motifs: a note on the architectural bias of recurrent networks
    Mikael Boden
    IEEE Trans Neural Netw 16:491-4. 2005
    ..and Hammer and Tino--offers superior access to motifs (sequential patterns) compared to the, in bioinformatics, standardly used feedforward neural networks...