research:publications

  1. Mora A, Schmidt C, Balderson B, Frezza C and Bodén M. SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer. https://www.biorxiv.org/content/10.1101/2022.07.02.498058v1
  2. Shen S et al. An integrated cell barcoding and computational analysis pipeline for scalable analysis of differentiation at single-cell resolution. https://doi.org/10.1101/2022.10.12.511862
  1. Prabhu A, Tule S, Chuvochina M, Bodén M, McIlroy SJ, Zaugg J and Rinke C. (Accepted) Machine learning and metagenomics identifies uncharacterized taxa inferred to drive biogeochemical cycles in a subtropical hypereutrophic estuary. ISME Communications. DOI 10.1093/ismeco/ycae067
  2. Tule S, Foley G, Zhao C, Forbes M and Bodén M. (Accepted) Optimal Phylogenetic Reconstruction of Insertion and Deletion Events. Bioinformatics/ISMB 2024 Proceedings. DOI 10.1093/bioinformatics/btae254 Pre-print
  3. Balderson B, Fane M, Harvey TJ, Piper M, Smith A and Bodén M. (2024) Systematic analysis of the Transcriptional Landscape of Melanoma Reveals Drug-target Expression Plasticity. Briefings in Functional Genomics. DOI 10.1093/bfgp/elad055
  4. Teshima M, Sutiono S, Döring M, Beer B, Boden M, Schenk G and Sieber V (2023) Development of a Highly Selective NAD+-Dependent Glyceraldehyde Dehydrogenase and its Application in Minimal Cell-Free Enzyme Cascades. ChemSusChem DOI 10.1002/cssc.202301132
  5. Balderson B, Piper M, Thor S and Bodén M. (2023) Cytocipher detects significantly different populations of cells in single cell RNA-seq data. Bioinformatics. 39(7):btad435. DOI 10.1093/bioinformatics/btad435biorxiv
  6. Sun Y, Shim W, Shen S, Sinniah E, Pham D, Su Z, Mizikovsky D, White MD, Ho JWK, Nguyen Q, Bodén M, Palpant NJ. (2023) Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity. Nucleic Acids Research. DOI 10.1093/nar/gkad307
  7. Afonso J et al. (2023) Repressive epigenetic mechanisms, such as the H3K27me3 histone modification, were predicted to affect muscle gene expression and its mineral content in Nelore cattle. Biochemistry and Biophysics Reports. DOI 10.1016/j.bbrep.2023.101420
  8. Bayaraa T et al. (2022) Structural and Functional Insight into the Mechanism of the Fe-S Cluster-Dependent Dehydratase from Paralcaligenes ureilyticus. Chemistry - A European Journal. DOI 10.1002/chem.202203140
  9. Foley G, Mora A, Ross CM, Bottoms S, Sützl L, Lamprecht ML, Zaugg J, Essebier A, Balderson B, Newell R, Thomson RES, Kobe B, Barnard RT, Guddat L, Schenk G, Carsten J, Gumulya Y, Rost B, Haltrich D, Sieber V, Gillam EMJ and Bodén M. (2022) Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP). PLoS Comput Biol. 18(10): e1010633. DOI 10.1371/journal.pcbi.1010633Pre-print
  10. Lv Y, Zheng S, Goldenzweig A, Liu F, Gao Y, Yang X, Kandale A, McGeary RP, Williams S, Kobe B, Schembri MA, Landsberg MJ, Wu B, Brück TB, Sieber V, Bodén M, Rao Z, Fleishman SJ, Schenk G and Guddat LW. (2022) Enhancing the Thermal and Kinetic Stability of Ketol-Acid Reductoisomerase, a Central Catalyst of a Cell-Free Enzyme Cascade for the Manufacture of Platform Chemicals. Applied Biosciences. 1(2):163-178. DOI 10.3390/applbiosci1020011
  11. Harris KL, Thomson RES et al. (2022) Ancestral sequence reconstruction of a cytochrome P450 family involved in chemical defence reveals the functional evolution of a promiscuous, xenobiotic-metabolizing enzyme in vertebrates. Molecular Biology and Evolution. DOI 10.1093/molbev/msac116
  12. Mora A, Rakar J, Monedero-Cobeta I, Yaghmaeian-Salmani B, Starkenberg A, Thor S, Bodén M. (2022) Variational autoencoding of gene landscapes during mouse CNS development uncovers layered roles of Polycomb Repressor Complex 2. Nucleic Acids Research. 50(3):1280–1296. DOI 10.1093/nar/gkac006 Pre-print in bioRxiv
  13. Yaghmaeian-Salmani B, Balderson B, Bauer S, Ekman H, Starkenberg A, Perlmann T, Piper M, Bodén M, Thor S. (2022) Selective Requirement for Polycomb Repressor Complex 2 in the Generation of Specific Hypothalamic Neuronal Sub-types. Development. 149(5):dev200076. DOI 10.1242/dev.200076 Pre-print in bioRxiv
  14. Ross CM, Foley G, Bodén M, Gillam EMJ. (2022) Using the Evolutionary History of Proteins to Engineer Insertion-Deletion Mutants from Robust, Ancestral Templates Using Graphical Representation of Ancestral Sequence Predictions (GRASP). Methods Molecular Biology. 2397:85-110. DOI 10.1007/978-1-0716-1826-4_6.
  15. Wilson L et al. (2021) Kinetic and Structural Characterization of the First B3 Metallo-β-Lactamase with an Active Site Glutamic Acid. Antimicrobial Agents and Chemotherapy. 65(10):e0093621. DOI 10.1128/AAC.00936-21.
  16. Kojic M et al. (2021) Elp2 mutations perturb the epitranscriptome and lead to a complex neurodevelopmental phenotype. Nature Communications. 12:2678. DOI 10.1038/s41467-021-22888-5
  17. Newell R, Pienaar R, Balderson B, Piper M, Essebier A and Bodén M (2021) ChIP-R: Assembling reproducible sets of ChIP-seq and ATAC-seq peaks from multiple replicates. Genomics. 113(4). DOI 10.1016/j.ygeno.2021.04.026 Pre-print in bioRxiv
  18. Shim WJ, Sinniah E et al. (2020) Conserved epigenetic regulatory logic infers genes governing cell identity. Cell Systems. 11(6):625-639.e13. DOI 10.1016/j.cels.2020.11.001Pre-print in bioRxiv
  19. Lai JS, Rost B, Kobe B and Bodén M (2020) Evolutionary model of protein secondary structure capable of revealing new biological relationships. Proteins. 88(9):1251-1259. DOI 10.1002/PROT.25898Pre-print
  20. O'Connor T, Grant CE, Bodén M and Bailey TL (2020) T-Gene: Improved target gene prediction. Bioinformatics. 36(12):3902–3904. DOI 10.1093/bioinformatics/btaa227Pre-print
  21. Fraser JA, et al. (2020) Common regulatory targets of NFIA, NFIX and NFIB during postnatal cerebellar development. The Cerebellum. 19:89–101. DOI 10.1007/s12311-019-01089-3
  22. Horsefield S, et al. (2019) NAD+ cleavage activity by animal and plant TIR domains in cell death pathways. Science 365(6455):793-799. DOI 10.1126/science.aax1911 Link direct to Science
  23. Foley G, Sützl L, D'Cunha SA, Gillam EMJ and Bodén M (2019). SeqScrub: A web tool for automatic cleaning and annotation of FASTA file headers for bioinformatic applications. Biotechniques. 67(2). DOI 10.2144/btn-2018-0188
  24. Littmann M, Goldberg T, Seitz S, Bodén M and Rost B (2019). Detailed prediction of protein sub-nuclear localization. BMC Bioinformatics. 20(1):205. DOI 10.1186/s12859-019-2790-9
  25. Sützl L, Foley G, Gillam EM, Bodén M and Haltrich D (2019). The GMC family of oxidoreductases revisited: analysis and evolution of fungal GMC oxidoreductases. Biotechnology for Biofuels. 12:118. DOI 10.1186/s13068-019-1457-0
  26. Fraser J, Essebier A, Brown A, Ayala Davila R, Sengar A, Tu A, Ensbey K, Day B, Scott M, Gronostajski R, Wainwright B, Bodén M, Harvey T and Piper M. (2019) Granule neuron precursor cell proliferation is regulated by NFIX and intersectin 1 during postnatal cerebellar development. Brain Structure and Function. 224:2, pp 811–827. DOI 10.1007/s00429-018-1801-3
  27. Gumulya Y, et al. (2019) Engineering thermostable CYP2D enzymes for biocatalysis using combinatorial libraries of ancestors for directed evolution (CLADE). ChemCatChem. 11:2, pp 841-850. DOI 10.1002/cctc.201801644
  28. Gumulya Y, et al. (2018) Engineering highly functional thermostable proteins using ancestral sequence reconstruction. Nature Catalysis. 1, pp 878–888. DOI 10.1038/s41929-018-0159-5 Free access
  29. Fletcher SJ, Bodén M, Mitter N and Carroll BJ (2018) SCRAM: a pipeline for fast index-free small RNA read alignment and visualization. Bioinformatics. 34:15, pp 2670–2672. DOI 10.1093/bioinformatics/bty161Free access
  30. Zaugg J, Gumulya Y, Bodén M, Mark AE and Malde AK (2018) Effect of Binding on Enantioselectivity of Epoxide Hydrolase. Chem Inf Model. 58 (3): 630–640. DOI 10.1021/acs.jcim.7b00353.
  31. Zaugg J, Gumulya Y, Malde AK and Bodén M (2017) Learning Epistatic Interactions from Sequence-Activity Data to Predict Enantioselectivity. J Comput Aided Mol Des. 31(12):1085–1096. DOI 10.1007/s10822-017-0090-x Free access
  32. Essebier A, Lamprecht M, Piper M and Bodén M (2017) Bioinformatics approaches to predict target genes from transcription factor binding data. Methods. Volume 131, Pages 111-119. DOI 10.1016/j.ymeth.2017.09.001.
  33. Bernhofer M, Goldberg T, Wolf S, Ahmed M, Zaugg J, Bodén M and Rost B (2017) NLSdb - major update for database of nuclear localization signals and nuclear export signals, Nucleic Acids Res. 46:D1, pp D503–D508. DOI 10.1093/nar/gkx1021
  34. Patrick R, Kobe B, Le Cao KA and Bodén M (2017) PhosphoPICK-SNP: Quantifying the effect of amino acid variants on protein phosphorylation, Bioinformatics. 33 (12): 1773-1781. DOI 10.1093/bioinformatics/btx072. Free access
  35. O'Connor TR, Bodén M and Bailey, TL (2017) CISMAPPER: predicting regulatory interactions from transcription factor ChIP-seq data. Nucleic Acids Res 45 (4): e19. DOI: 10.1093/nar/gkw956.
  36. Williams SJ, Yin L, Foley G, Casey LW, Outram MA, Ericsson DJ, Lu J, Bodén M, Dry I and Kobe B (2016) Structure and function of the TIR domain from the grape NLR protein RPV1. Frontiers in Plant Science. DOI: 10.3389/fpls.2016.01850
  37. Fraser J, Essebier A, Gronostajski RM, Bodén M, Wainwright BJ, Harvey TJ and Piper M (2016) Cell type-specific expression of NFIX in the developing and adult cerebellum. Brain Structure and Function. 222:5, pp 2251–2270. DOI: 10.1007/s00429-016-1340-8
  38. Patrick R, Horin C, Kobe B, Le Cao KA and Bodén M (2016) Prediction of kinase-specific phosphorylation sites through an integrative model of protein context and sequence. BBA - Proteins and Proteomics. 1864(11):1599-608. DOI:10.1016/j.bbapap.2016.08.001.
  39. Essebier A, Vera Wolf P, Cao MD, Carroll BJ, Balasubramanian S and Bodén M (2016) Statistical enrichment of epigenetic states around triplet repeats that undergo expansions. Frontiers in Neuroscience. 10:92. DOI: 10.3389/fnins.2016.00092 (Open access)
  40. Cao MD, Allison L, Dix TI and Bodén M (2016) Robust Estimation of Evolutionary Distances with Information Theory. Molecular Biology and Evolution. 33 (5): 1349-1357. DOI: 10.1093/molbev/msw019
  41. Huang W, Johnston W, Bodén M and Gillam EM (2016) ReX - A suite of computational tools for the design, visualization and analysis of chimeric protein libraries. BioTechniques. Vol. 60, No. 2, pp. 91–94. DOI: 10.2144/000114381.
  42. Oyarzun P, Ellis JJ, Gonzalez-Galarza FF, Jones AR, Middleton D, Bodén M and Kobe B (2015) A bioinformatics tool for epitope-based vaccine design that accounts for human ethnic diversity: Application to emerging infectious diseases. Vaccine. 33(10):1267-73. DOI: 10.1016/j.vaccine.2015.01.040
  43. Patrick R, Le Cao KA, Kobe B and Bodén M (2015) PhosphoPICK: Modelling cellular context to map kinase-substrate phosphorylation events. Bioinformatics. 31(3):382-389. DOI: 10.1093/bioinformatics/btu663
  44. Cao MD, Balasubramanian S and Bodén M (2015) Sequencing technologies and tools for short tandem repeat variation detection, Briefings in Bioinformatics. 16 (2): 193-204. DOI:10.1093/bib/bbu001.
  45. Rona G, Borsos M, Ellis J, Mehdi A, Christie M, Kornyei Z, Neubrandt M, Toth J, Bozoky Z, Buday L, Madarasz E, Bodén M, Kobe B and Vertessy B (2014). Dynamics of re-constitution of the human nuclear proteome after cell division is regulated by NLS-adjacent phosphorylation. Cell Cycle. 13(22). DOI: 10.4161/15384101.2014.960740 (author eprint)
  46. Mehdi AM, Patrick R, Bailey TL and Bodén M (2014) Predicting the dynamics of protein abundance, Molecular & Cellular Proteomics. May;13(5):1330-40. DOI:10.1074/mcp.M113.033076.
  47. Cao MD, Tasker E, Willadsen K, Imelfort M, Vishwanathan S, Sureshkumar S, Balasubramanian S and Bodén M (2014) Inferring Short Tandem Repeat Variation from Paired-End Short Reads, Nucleic Acids Research. Feb;42(3):e16. DOI: 10.1093/nar/gkt1313.
  48. Mazgut J, Tino P, Bodén M and Yan H (2014) Dimensionality Reduction and Topographic Mapping of Binary Tensors, Pattern Analysis and Applications. 17(3):497-515. DOI: 10.1007/s10044-013-0317-y.
  49. Chang C-W, Counago RLM, Williams S, Bodén M and Kobe B (2013) The distribution of different classes of nuclear localization signals (NLSs) in different organisms and the utilization of the minor NLS-binding site in plant nuclear import factor importin-alpha, Plant Signaling & Behavior, 8(10). DOI: 10.4161/psb.25976.
  50. Chang C-W, Counago RLM, Williams S, Bodén M and Kobe B (2013) Distinctive Conformation of Minor Site-Specific Nuclear Localization Signals Bound to Importin-alpha, Traffic. Nov;14(11):1144-54. DOI: 10.1111/tra.12098
  51. Oyarzun P, Ellis JJ, Bodén M and Kobe B (2013) PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of human HLA class II DR protein diversity, BMC Bioinformatics, 14:52. DOI: 10.1186/1471-2105-14-52
  52. Willadsen K, Cao MD, Wiles J, Balasubramanian SK and Bodén M (2013) Repeat-encoded poly-Q tracts show statistical commonalities across species, BMC Genomics 14:76. DOI: 10.1186/1471-2164-14-76
  53. Mehdi A, Sehgal S, Kobe B, Bailey TL and Bodén M (2013) DLocalMotif: A discriminative approach for discovering local motifs in protein sequences, Bioinformatics, 29(1):39-46. DOI: 10.1093/bioinformatics/bts654.
  54. Chang C-W, Counago RLM, Williams S, Bodén M and Kobe B (2012) Crystal Structure of Rice Importin-alpha and Structural Basis of its Interaction with Plant-Specific Nuclear Localization Signals, The Plant Cell, 24(12):5074-88. doi: 10.1105/tpc.112.104422. (Access)
  55. Patrick R, Le Cao K-A, Davis M, Kobe B and Bodén M (2012) Mapping the stabilome: a novel computational method for classifying metabolic protein stability. BMC Systems Biol 6(1):60. (Open access.)
  56. Kobe B & Bodén M (2012) Computational modelling of linear motif-mediated protein interactions. Curr Top Med Chem. 12 (14), 1553-1561 [PMID: 22827524]
  57. Willadsen K, Mohamad N, and Bodén M (2012) NSort/DB: An intra-nuclear compartment protein database. Genomics, Proteomics & Bioinformatics 10(4):226-229. DOI: 10.1016/j.gpb.2012.07.001
  58. Madala PK, Fairlie DP and Bodén M (2012) Matching cavities in G protein-coupled receptors to infer ligand-binding sites. Journal of Chemical Information & Modeling. 52(5):1401-10.
  59. Bauer DC, Willadsen K, Buske FA, Le Cao K-A, Bailey TL, Dellaire G and Bodén M (2011) Sorting the nuclear proteome. Bioinformatics. 27(13):i7-14. (Open access.)
  60. Mehdi A, Sehgal S, Kobe B, Bailey TL and Bodén M (2011) A probabilistic model of nuclear import of proteins. Bioinformatics. 27(9):1239-46.
  61. Marfori M, Mynott A, Ellis JJ, Mehdi AM, Saunders NF, Curmi PM, Forwood JK, Bodén M and Kobe B (2011) Molecular basis for specificity of nuclear import and prediction of nuclear localization. Biochim Biophys Acta 1813(9):1562-77.
  62. You L, Brusic VL, Gallagher M and Bodén M (2010) Using Gaussian process with test rejection to detect T-cell epitopes in pathogen genomes. IEEE/ACM Trans Comp Biol Bioinformatics 7(4):741-751.
  63. Mohamad N and Bodén M (2010) The proteins of intra-nuclear bodies: a data-driven analysis of sequence, interaction and expression. BMC Systems Biology 4:44. (Open access.)
  64. Buske FA, Bodén M, Bauer DC and Bailey TL (2010) Assigning roles to DNA regulatory motifs using comparative genomics. Bioinformatics 26:860-6. (Open access.)
  65. Bauer DC, Buske FA, Bailey TL and Bodén M (2010) Predicting SUMOylation sites in developmental transcription factors of Drosophila melanogaster. Neurocomputing. 73(13-15): 2300-2307.
  66. Bodén M, Dellaire G, Burrage K and Bailey TL (2010) A Bayesian network model of proteins' association with Promyelocytic leukemia (PML) nuclear bodies. J Comp Biol 17(4): 617-30.
  67. Bailey T, Bodén M, Whitington T and Machanick P (2010) The value of position-specific priors in motif discovery using MEME. BMC Bioinformatics 11:179. (Open access.)
  68. Buske FA, Maetschke S and Bodén M (2009) It's about time: signal recognition in staged models of protein translocation. Pattern Recognit 42(4): 567-574.
  69. Buske FA, Thier R, Gillam EM and Bodén M (2009) In silico characterisation of protein chimeras: relating sequence and function within the same fold. Proteins 77(1), pp. 111-120.
  70. Bailey TL, Bodén M, Buske FA, Frith M, Grant CE, Clementi L, Ren J, Li WW and Noble WS (2009) MEME Suite: tools for motif discovery and searching. Nucleic Acids Res 37:W202-8. (Open access.)
  71. Bodén M and Teasdale RD (2008) Determining nucleolar association from sequence by leveraging protein-protein interactions. J Comp Biol 15(3): 291-304.
  72. Bodén M and Bailey TL (2008) Associating transcription factor binding site motifs with target GO terms and target genes. Nucleic Acids Res 36(12): 4108-4117. (Open access.)
  73. Bodén M and Bodén M (2007) Evolving spelling exercises to suit individual student needs. Appl Soft Comput 7: 126-135.
  74. Suksawatchon J, Lursinsap C and Bodén M (2007) Computing the reversal distance between genomes in the presence of multi-gene families via binary integer programming. J Bioinformatics Comp Biol 5(1): 117-133.
  75. Hawkins J, Mahony D, Maetschke S, Wakabayashi M, Teasdale RD and Bodén M (2007) Identifying novel peroxisomal proteins. Proteins 69(3): 606-616.
  76. Hawkins J, Davis L and Bodén M (2007) Predicting nuclear localization. J Proteome Res 6(4): 1402-1409.
  77. Bauer, D., Bodén M., Thier, R. and Gillam, E. M. STAR: Predicting recombination sites from amino acid sequence. BMC Bioinformatics, 7:437, 2006. (Open access.)
  78. Bodén, M. and Bailey, T. L. Identifying sequence regions undergoing conformational change via predicted continuum secondary structure. Bioinformatics. 22(15): 1809-1814, 2006. (Open access.)
  79. Bodén, M., Yuan, Z. and Bailey, T. L. Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures. BMC Bioinformatics. 7:68, 2006. (Open access.)
  80. Yuan, Z., Zhang, F., Davis, M. J., Bodén M. and Teasdale, R. D. Predicting the solvent accessibility of transmembrane residues from protein sequence. Journal of Proteome Research. 5(5):1063-1070, 2006.
  81. Hawkins, J. and Bodén M. Detecting and sorting targeting peptides with recurrent networks and support vector machines. Journal of Bioinformatics and Computational Biology, 4(1), 2006.
  82. Bodén, M. and Hawkins, J. Prediction of subcellular localisation using sequence-biased recurrent networks. Bioinformatics. 21(10):2279-2286, 2005.
  83. Hawkins, J. and Bodén M. The applicability of recurrent neural networks for biological sequence analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2(3): 243-253, 2005.
  84. Bodén, M. and Hawkins, J. Improved access to sequential motifs: A note on the architectural bias of recurrent networks. IEEE Transactions on Neural Networks. 16(2), 2005.
  85. Bodén, M. Generalization by symbolic abstraction in cascaded recurrent networks, Neurocomputing, 57, pp. 87-104, 2004.
  86. Bodén, M. and Blair, A. Learning the dynamics of embedded clauses, Applied Intelligence: Special issue on natural language and machine learning, 19(1/2), pp. 51-63, 2003.
  87. Bodén, M. and Wiles, J. On learning context free and context sensitive languages, IEEE Transactions on Neural Networks. 13(2), pp. 491-493, 2002.
  88. Bodén, M. and Wiles, J., Context-free and context-sensitive dynamics in recurrent neural networks, Connection Science, 12 (3/4), pp. 197-210, 2000.
  89. Bodén, M. and Niklasson, L., Semantic systematicity and context in connectionist Networks, Connection Science, 12 (2), pp. 111-142, 2000.
  1. Mazgut J, Tino P, Bodén M and Yan H (2010) Multilinear Decomposition and Topographic Mapping of Binary Tensors Artificial Neural Networks - ICANN 2010. K Diamantaras, W Duch, LS Iliadis (Eds.). Lecture Notes in Computer Science, Vol. 6352. pp. 317-326. Springer-Verlag, 2010.
  2. Arieshanti I, Bodén M, Maetschke S and Buske F (2009) Detecting sequence and structure homology via an integrative kernel: A case-study in recognizing enzymes. Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 46-52.
  3. Bodén M (2008) Predicting nucleolar proteins using support-vector machines. Proceedings of the Asia-Pacific Bioinformatics Conference APBC 2008, Brazma, A., Miyano S. and Akutsu, T. (eds.) pp. 19-28, Imperial College Press.
  4. Bauer D, Buske FA and Bodén M (2008) Predicting SUMOylation Sites. Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2008), Chetty M, Ngom A and Ahmad S (eds.), Springer Lecture Notes in Computer Science, vol. 5265, pp. 28-40.
  5. You L, Zhang P, Bodén M and Brusic VL (2007) Understanding Prediction Systems for HLA-Binding Peptides and T-cell Epitope Identification. Proceedings of the 2nd IAPR Workshop on Pattern Recognition in Bioinformatics, LNBI 4774, pp. 337-348, Singapore, Springer Verlag.
  6. Buske F and Bodén M (2007) Decoupling signal recognition from sequence models of protein secretion. Proceedings of 2007 International Symposium on Computational Models for Life Sciences CMLS07, Pham, T. and Zhou, X. (eds), pp. 147-156, American Institute of Physics.
  7. Dufton L and Bodén M (2007) Reducing the number of support vectors to allay inefficiency of large-scale models in computational biology. Proceedings of 2007 International Symposium on Computational Models for Life Sciences CMLS07, Pham T and Zhou X (eds), pp. 340-348, American Institute of Physics.
  8. Maetschke S, Gallagher M and Bodén M (2007) A Comparison of Sequence Kernels for Localization Prediction of Transmembrane Proteins. Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 367-372.
  9. Davis, L., Hawkins, J., Maetschke, S. and Bodén M., Comparing SVM sequence kernels: A protein subcellular localization theme. In Proceedings of the Workshop on Intelligent Systems for Bioinformatics, CRPIT (vol. 73), 2006.
  10. Maetschke, S., Bodén M. and Gallagher, M., Higher order HMMs for Localization Prediction of Transmembrane Proteins. In Proceedings of the Workshop on Intelligent Systems for Bioinformatics, CRPIT (vol. 73), 2006.
  11. Hawkins, J. and Bodén M., Multi-stage Redundancy Reduction: Effective Utilisation of Small Protein Data Sets. In Proceedings of the Workshop on Intelligent Systems for Bioinformatics, CRPIT (vol. 73), 2006.
  12. Bodén, M. and Hawkins, J. Evolving discriminative motifs for recognizing proteins imported to the peroxisome via the PTS2 pathway. In Proceedings of the Congress on Evolutionary Computation. 2006.
  13. Bauer, D., Bodén M., Thier, R. and Yuan, Z. Predicting structural disruption of proteins caused by crossover. In Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 514-520. San Diego, 2005.
  14. Hawkins, J. and Bodén M. Predicting Peroxisomal proteins. In Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 469-474. San Diego, 2005.
  15. Suksawatchon, J., Lursinsap, C. and Bodén, M., Heuristic Algorithm for Computing Reversal Distance with Multi-Gene Families via Binary Integer Programming. In Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp. 187-193. San Diego, 2005.
  16. Wakabayashi, M., Hawkins, J., Maetschke, S. and Bodén M., Exploiting sequence dependencies in the prediction of peroxisomal proteins. In Intelligent Data Engineering and Automated Learning - IDEAL 2005, pp. 454-461, 2005.
  17. Bodén, M. and Hawkins, J. Detecting residues in targeting peptides. In Proceedings of the Asia-Pacific Bioinformatics Conference. pp. 131-140. Imperial College Press. Singapore. 2005.
  18. Maetschke, S., Towsey, M. and Bodén M. BLOMAP: An encoding of amino acids which improves signal peptide cleavage prediction. In Proceedings of the Asia-Pacific Bioinformatics Conference. pp. 141-150. Imperial College Press. Singapore. 2005.
  19. Bodén, M. Using evolutionary noise to improve prediction of rapidly evolving targeting peptides. In Proceedings of the 2003 Congress on Evolutionary Computation (CEC 2003), Canberra, 2003.
  20. Bodén, M. Generalization by structural properties from sparse nested symbolic data. In Proceedings of ESANN 2002 – Special session on Perspectives on Learning with Recurrent Networks, pp. 377-382, 2002.
  21. Bodén, M., Jacobsson, H. and Ziemke, T. (2000), Evolving context-free language predictors. In GECCO-2000: Proceedings of the Genetic and Evolutionary Computation Conference.
  1. Zaugg J, Gumulya Y, Gillam EMJ and Bodén M. (2014) Computational Tools for Directed Evolution: A Comparison of Prospective and Retrospective Strategies. Ackerley et al. (eds). Methods Mol Biol. 2014;1179:315-33. DOI: 10.1007/978-1-4939-1053-3_21
  2. Tino P, Hammer B and Bodén M (2007) Markovian bias of neural-based architectures with feedback connections. Perspectives of Neural-Symbolic Integration, Hitzler and Hammer (eds), pp. 95-133. Springer Verlag.
  3. Wiles, J., Blair, A. and Bodén M. (2001) Representation Beyond Finite States: Alternatives to Push-Down Automata, in A Field Guide to Dynamical Recurrent Networks, Kolen, J. F. and Kremer, S. C. (eds.), IEEE Press, pp. 129-142.

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  • research/publications.txt
  • Last modified: 2024/05/03 20:04
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