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Biography

Dr. Lukasz Kurgan

University of Alberta, Canada

Associate Professor


Email: lkurgan@ece.ualberta.ca


Qualifications


2000–2003  Ph.D., Department of Computer Science, University of Colorado at Boulder, USA

1999–2000  Ph. D. candidate, University of Toledo, USA

1994–1999  M.Sc., AGH University of Science and Technology, Poland, Computer Science and Electrical Engineering


Publications (Selected)

  1. Kurgan LA, Razib AA, Aghakhani S, Dick S, Mizianty M, Jahandideh S, 2009. CRYSTALP2: Sequence-based Protein Crystallization Propensity Prediction. BMC Structural Biology, 9:50
  2. Chen K, Kurgan LA, 2009. Investigation of Atomic Level Patterns in Protein - Small Ligand Interactions. PLoS 3. ONE, 4(2):e4473 Zhang H, Zhang T, Chen K, Shen S, Ruan J, Kurgan LA, 2009. On the Relation between Residue Flexibility and Local Solvent Accessibility in Proteins. Proteins: Structure, Function, and Bioinformatics, 76(3):617-636
  3. Jiang Y, Iglinski P, Kurgan LA, 2009. Prediction of Protein Folding Rates from Primary Sequences using Hybrid Sequence Representation. Journal of Computational Chemistry,30(5):772-783
  4. Chen K, Jiang Y, Du L, Kurgan LA, 2009. Prediction of Integral Membrane Protein Type by Collocated Hydrophobic Amino Acid Pairs. Journal of Computational Chemistry, 30(1):163-172
  5. Gehrke AS, Sun S, Kurgan LA, Ahn N, Resing K, Kafadar K, Cios KJ, 2008. Improved Machine Learning Method for Analysis of Gas Phase Chemistry of Peptides. BMC Bioinformatics,9:515
  6. Zheng C, Kurgan LA, 2008. Prediction of ß-turns at Over 80% Accuracy Based on an Ensemble of Predicted Secondary Structures and Multiple Alignments. BMC Bioinformatics, 9:430
  7. Zhang T, Zhang H, Chen K, Shen S, Ruan J, Kurgan LA, 2008. Accurate Sequence-based Prediction of Catalytic Residues. Bioinformatics, 24(20):2329-2338
  8. Ruan J, Chen H, Kurgan LA, Chen K, Kang C, Pu P, 2008. HuMiTar: A sequence-based Method for Prediction of Human microRNA Targets. Algorithms for Molecular Biology, 3:16
  9. Zhang H, Zhang T, Chen K, Shen S, Ruan J, Kurgan LA, 2008. Sequence Based Residue Depth Prediction Using Evolutionary Information and Predicted Secondary Structure. BMC Bioinformatics, 9:388
  10. Chen K, Huzil T, Friedman H, Ramachandran P, Antoniou A, Tuszynski J, Kurgan LA, 2008. Identification of Tubulin Drug Binding Sites and Prediction of Relative Differences in Binding Affinities to Tubulin Isotypes Using Digital Signal Processing. Journal of Molecular Graphics and Modelling, 27(4):497-505
  11. Kurgan LA, Cios KJ, Zhang H, Zhang T, Chen K, Shen S, Ruan J, 2008. Sequence-based Methods for Real Value Predictions of Protein Structure. Current Bioinformatics, 3(3):183-196
  12. Campbell K, Kurgan LA, 2008. Sequence-only Based Prediction of ß-turn Location and Type Using Collocation of Amino Acid Pairs. Open Bioinformatics Journal, 2:37-49
  13. Kurgan LA, Cios KJ, Chen K, 2008. SCPRED: Accurate Prediction of Protein Structural Class for Sequences of Twilight-zone Similarity with Predicting Sequences. BMC Bioinformatics,9:226
  14. Farhangfar A, Kurgan LA, Dy J, 2008. Impact of Imputation of Missing Values on Classification Error for Discrete Data. Pattern Recognition, 41(12):3692-3705
  15. Chen K, Kurgan M, Kurgan LA, 2008. Sequence Based Prediction of Relative Solvent Accessibility Using Two-stage Support Vector Regression with Confidence Values. Journal of Biomedical Science and Engineering, 1(1):1-9
  16. Kurgan LA, Zhang T, Zhang H, Shen S, Ruan J, 2008. Secondary Structure Based Assignment of the Protein Structural Classes. Amino Acids, 35(3):551-564
  17. Kedarisetti K, Dick S, Kurgan LA, 2008. Searching for Factors that Distinguish Disease-prone and Disease-resistant Prions via Sequence Analysis. Bioinformatics and Biology Insights,2:133-144
  18. Golmohammadi SK, Kurgan LA, Crowley B, Reformat M, 2008. Amino Acid Sequence Based Method for Prediction of Cell Membrane Protein Types. International Journal of Hybrid Information Technology, 1(1):95-109
  19. Bass SD, Kurgan LA, 2008. Discovery of Factors Influencing Patent Value Based on Machine Learning in Patents in the Field of Nanotechnology. Scientometrics, accepted
  20. Chen K, Kurgan LA, Ruan J, 2008. Prediction of Protein Structural Class Using Novel Evolutionary Collocation Based Sequence Representation. Journal of Computational Chemistry,29(10):1596-1604
  21. Kurgan LA, 2008. On the Relation between the Predicted Secondary Structure and the Protein Size. Protein Journal, 24(4):234-239 Stach W, Kurgan LA, Pedrycz W, 2008. Numerical and Linguistic Prediction of Time Series with the Use of Fuzzy Cognitive Maps. IEEE Transactions on Fuzzy Systems, 16(1):61-72
  22. Rak R, Kurgan LA, Reformat M, 2008. A Tree-projection-based Algorithm for Multi-label Recurrent-item Associative-Classification Rule Generation. Data and Knowledge Engineering,64(1):171-197
  23. Chen K, Kurgan LA, 2007. PFRES: Protein Fold Classification by Using Evolutionary Information and Predicted Secondary Structure. Bioinformatics, 23(21):2843-2850
  24. Chen K, Kurgan LA, Ruan J, 2007. Prediction of Flexible/Rigid Regions in Proteins from Sequences Using Collocated Amino Acid Pairs. BMC Structural Biology, 7:25
  25. Kurgan LA, Chen K, 2007. Prediction of Protein Structural Class for the Twilight Zone Sequences. Biochemical and Biophysical Research Communications, 357(2):453-460
  26. Rak R, Kurgan LA, Reformat M, 2007. xGENIA: A comprehensive OWL ontology based on the GENIA corpus. Bioinformation, 1(9):360-362
  27. Homaeian L, Kurgan LA, Cios KJ, Ruan J, Chen K, 2007. Prediction of Protein Secondary Structure Content for the Twilight Zone Sequences. Proteins: Structure, Function, and Bioinformatics, 69(3):486-498
  28. Chen K, Kurgan LA, Rahbari M, 2007. Prediction of Protein Crystallization Using Collocation of AA pairs. Biochemical and Biophysical Research Communications, 355(3):764-769
  29. Huzil T, Chen K, Kurgan LA, Tuszynski J, 2007. The Roles of Beta-Tubulin Mutations and Isotype Expression in Acquired Drug Resistance. Cancer Informatics, 3:159-181
  30. Rak R, Kurgan LA, Reformat M, 2007. Multilabel Associative Classification Categorization of MEDLINE Articles into MeSH Keywords. IEEE Engineering in Medicine and Biology Magazine,special issue on Machine Learning in the Life Sciences, 26(2):47-55
  31. Kurgan LA, Stach W, Ruan J, 2007. Novel Scales Based on Hydrophobicity Indices for Secondary Protein Structure. Journal of Theoretical Biology, 248(2):354-366 Farhangfar A, Kurgan LA, Pedrycz W, 2007. A Novel Framework for Imputation of Missing Values in Databases. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 37(5):692-709
  32. Kurgan LA, Kedarisetti K, 2006. Sequence Representation and Prediction of Protein Secondary Structure for Structural Motifs in Twilight Zone Proteins. Protein Journal, 25(7-8):463-474
  33. Kedarisetti K, Kurgan LA, Dick S, 2006. Classifier Ensembles for Protein Structural Class Prediction with Varying Homology. Biochemical and Biophysical Research Communications,348(3):981-988
  34. Kedarisetti K, Kurgan LA, Dick S, 2006. A Comment on 'Prediction of protein structural classes by a new measure of information discrepancy'. Computational Biology and Chemistry,30(5):393-394
  35. Ruan J, Chen K, Tuszynski J, Kurgan LA, 2006. Quantitative Analysis of the Conservation of the Tertiary Structure of Protein Segments. Protein Journal, 25(5):301-315
  36. Chen K, Ruan J, Kurgan LA, 2006. Prediction of Three Dimensional Structure of Calmodulin. Protein Journal, 40. 25(1):57-70
  37. Kurgan LA, Homaeian L, 2006. Prediction of Structural Classes for Protein Sequences and Domains - Impact of Prediction Algorithms, Sequence Representation and Homology, and Test Procedures on Accuracy. Pattern Recognition, special issue on Bioinformatics, 39(12):2323-2343
  38. Swiercz W, Cios KJ, Staley K, Kurgan LA, Accurso F, Sagel S, 2006. A New Synaptic Plasticity Rule for Networks of Spiking Neurons. IEEE Transactions on Neural Networks, 17(1):94-105
  39. Kurgan LA, Musilek P, 2006. A Survey of Knowledge Discovery and Data Mining Process Models. Knowledge Engineering Review, 21(1):1-24
  40. Kurgan LA, Cios KJ, Dick S, 2006. Highly Scalable and Robust Rule Learner: Performance Evaluation and Comparison. IEEE Transactions on Systems, Man, and Cybernetics, Part B,36(1):32-53
  41. Ruan J, Wang K, Yang J, Kurgan LA, Cios KJ, 2005. Highly Accurate and Consistent Method for Prediction of Helix and Strand Content from Primary Protein Sequences. Artificial Intelligence in Medicine, special issue on Computational Intelligence Techniques in Bioinformatics, 35(1-2):19-35
  42. Stach W, Kurgan LA, Pedrycz W, Reformat M, 2005. Genetic Learning of Fuzzy Cognitive Maps. Fuzzy Sets and Systems, 153(3):371-401
  43. Stach W, Kurgan LA, Pedrycz W, Reformat M, 2004. Learning Fuzzy Cognitive Maps with Required Precision using Genetic Algorithm Approach. Electronics Letters, 40(24):1519-1520
  44. Kurgan LA, Cios KJ, 2004. CAIM Discretization Algorithm. IEEE Transactions on Data and Knowledge Engineering, 16(2):145-153
  45. Cios KJ, Kurgan LA, 2004. CLIP4: Hybrid Inductive Machine Learning Algorithm that Generates Inequality Rules. Information Sciences, special issue on Soft Computing Data Mining, 163(1-3):37-83
  46. Kurgan LA, Cios KJ, Tadeusiewicz R, Ogiela M, Goodenday LS, 2001. Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis. Artificial Intelligence in Medicine,23(2):149-169
  47. Cios K, Pedrycz W, Swiniarski R, Kurgan LA, 2007. Data Mining: A Knowledge Discovery Approach, Springer, ISBN 0-387-33333-9, 700 pages
  48. Wani A, Chen X, Casasent D, Kurgan LA, Hu T, Hafeez K, (Eds.) 2008. Proceedings of the Seventh International Conference on Machine Learning and Applications (ICMLA'08), IEEE Press, ISBN 978-0-7695-3495-4, 905 pages
  49. Wani A, Kantardzic M, Li T, Liu Y, Kurgan LA, Ye J, Ogihara M, Sagiroglu S, Chen X, Peterson L, Hafeez K, (Eds.) 2007. Proceedings of the Sixth International Conference on Machine Learning and Applications (ICMLA'07), IEEE Press, ISBN 0-7695-3069-9, 638 pages
  50. Kurgan LA, Reformat M, Cios KJ, (Eds.) 2007. Editorial for special issue on Machine Learning in the Life Sciences, IEEE Engineering in Medicine and Biology Magazine, IEEE Press
  51. Reformat M, Kurgan LA, (Eds.) 2007. Proceedings of the Human Centric Computing and Data Processing (HC2DP 2007) Symposium 25 pages
  52. Wani A, Li T, Kurgan LA, Ye J, Liu J, (Eds.) 2006. Proceedings of the Fifth International Conference on Machine Learning and Applications (ICMLA'06), IEEE Press, ISBN 0-7695-2735-3, 303 pages
  53. Musilek P, Reformat M, Kurgan LA, (Eds.) 2005. Editorial for special issue on Biologically Inspired Computing and Computers in Biology, Neural Networks World
  54. Wani A, Milanowa M, Kurgan LA, Reformat M, Hafeez K, (Eds.) 2005. Proceedings of the Fourth International Conference on Machine Learning and Applications (ICMLA'05), IEEE Press, ISBN 0-7695-2495-8, 412 pages
  55. Dick S, Kurgan LA, Musilek P, Pedrycz W, Reformat M, (Eds.) 2004. Proceedings of the 2004 North American Fuzzy Information Proceesing Society (NAFIPS'04) Conference, IEEE Press, ISBN 0-7803-8376-1, 1024 pages
  56. Kurgan LA, Musilek P, Pedrycz W, Reformat M, (Eds.) 2005. Proceedings of the Human Centric Computing (HC2 2005) Symposium 71 pages

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