John Hawkins

Summary

Affiliation: University of Queensland
Country: Australia

Publications

  1. ncbi 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 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 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 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 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. ncbi 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 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 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 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 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 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 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. ncbi 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
    ..Among the three scanning algorithms that we test, the MONKEY algorithm has the highest accuracy for predicting yeast TFBSs...
  7. ncbi 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...