Biomine Kurgan Lab

College of Engineering

Publications

underline identifies corresponding author(s) CAPS ANNOTATES TRAINEES IN MY LAB
bold font shows representative high impact journal articles that originated from my lab

Journal Papers

    2025

  1. BASU S, YU J, Kihara D, Kurgan L, 2025. Twenty Years of Advances in Prediction of Nucleic Acid Binding Residues in Protein Sequences. Briefings in Bioinformatics, DOI: 10.1093/bib/bbaf016
  2. Song J and Kurgan L, 2025. Two Decades of Advances in Sequence-based Prediction of MoRFs, Disorder-to-order Transitioning Binding Regions. Expert Review of Proteomics, DOI: 10.1080/14789450.2025.2451715

    3. 2024

  3. Zhang F and Kurgan L, 2024. Evaluation of Predictions of Disordered Binding Regions in the CAID2 Experiment. Computational and Structural Biotechnology Journal, 27:78-88
  4. Pan T, Bi Y, Wang X, Zhang Y, Webb GI, Gasser RB, Kurgan L, Song J, 2024. SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations. Genomics, Proteomics and Bioinformatics, DOI:10.1093/gpbjnl/qzae094
  5. Zhang J, BASU S, Zhang F, Kurgan L, 2024. MERIT: Accurate Prediction of Multi Ligand-binding Residues with Hybrid Deep Transformer Network, Evolutionary Couplings and Transfer Learning. Journal of Molecular Biology, DOI: 10.1016/j.jmb.2024.168872
  6. BASU S, Kurgan L, 2024. Taxonomy-specific Assessment of Intrinsic Disorder Predictions at Residue and Region Levels in Higher Eukaryotes, Protists, Archaea, Bacteria and Viruses. Computational and Structural Biotechnology Journal, 23:1968-1977
  7. Wang K, Hu G, BASU S, Kurgan L, 2024. flDPnn2: Accurate and Fast Predictor of Intrinsic Disorder in Proteins. Journal of Molecular Biology, DOI: 10.1016/j.jmb.2024.168605
  8. Wang K, Gang H, Wu Z, Uversky VN, Kurgan L, 2024. Assessment of Disordered Linker Predictions in the CAID2 Experiment. Biomolecules, 14(3):287
  9. Zhang J, BASU S, Kurgan L, 2024. HybridDBRpred: Improved Sequence-based Prediction of DNA-binding Amino Acids Using Annotations from Structured Complexes and Disordered Proteins. Nucleic Acids Research, 52(2):e10
  10. BASU S, Zhao B, BIRO B, Faraggi E, Gsponer J, Hu G, Kloczkowski A, Malhis N, Mirdita M, Söding J, Steinegger M, Wang W, Wang K, Xu D, Zhang J, Kurgan L, 2024. DescribePROT in 2023: More, Higher-quality and Experimental Annotations and Improved Data Download Options. Nucleic Acids Research, 52(D1):D426–D433

    11. 2023

  11. Song J, Kurgan L, Song J, 2023. Availability of Web Servers Significantly Boosts Citations Rates of Bioinformatics Methods for Protein Function and Disorder Prediction. Bioinformatics Advances, 3(1):vbad184
  12. Li F, Wang C, Guo X, Akutsu T, Webb GI, Coin L, Kurgan L, Song J, 2023. ProsperousPlus: a One-stop and Comprehensive Platform for Accurate Protease-specific Substrate Cleavage Prediction and Machine-learning Model Construction. Briefings in Bioinformatics, 24(6):bbad372
  13. Kurgan L, Hu G, Wang K, GHADERMARZI S, Zhao B, Malhis N, Erdős G, Gsponer J, Uversky VN, Dosztányi Z, 2023. Tutorial: A Guide for the Selection of Fast and Accurate Computational Tools for the Prediction of Intrinsic Disorder in Proteins. Nature Protocols, 18:3157–3172
  14. BASU S, Hegedűs T, Kurgan L, 2023. CoMemMoRFPred: Sequence-based Prediction of MemMoRFs by Combining Predictors of Intrinsic Disorder, MoRFs and Disordered Lipid-binding Regions. Journal of Molecular Biology, 435(21):168272
  15. ZHAO B, GHADERMARZI S, Kurgan L, 2023. Comparative Evaluation of AlphaFold2 and Disorder Predictors for Prediction of Intrinsic Disorder, Disorder Content and Fully Disordered Proteins. Computational and Structural Biotechnology Journal, 21:3248-3258
  16. Del Conte A, Bouhraoua A, Mehdiabadi M, Clementel D, Monzon AM, CAID Predictors (including Kurgan L), Tosatto S, Piovesan D, 2023. CAID Prediction Portal: a Comprehensive Service for Predicting Intrinsic Disorder and Binding Regions in Proteins. Nucleic Acids Research, 51:W1–W62
  17. BASU S, Gsponer J, Kurgan L, 2023. DEPICTER2: A Comprehensive Webserver for Intrinsic Disorder and Disorder Function Prediction. Nucleic Acids Research, 51:W141–W147
  18. BASU S, Kihara D, Kurgan L, 2023. Computational Prediction of Disordered Binding Regions. Computational and Structural Biotechnology Journal, 21:1487-1497
  19. Wang M, Kurgan L, Li M, 2023. A Comprehensive Assessment and Comparison of Tools for HLA Class I Peptide-binding Prediction. Briefings in Bioinformatics, 24(3):bbad150
  20. Zhang F, Li M, Zhang J, Shi W, Kurgan L, 2023. DeepPRObind: Modular Deep Learner that Accurately Predicts Structure and Disorder-annotated Protein Binding Residues. Journal of Molecular Biology, 435(14):167945
  21. Wu Z, BASU S, Wu X, Kurgan L, 2022. qNABpredict: Quick, Accurate and Taxonomy-aware Sequence-based Prediction of Content of Nucleic Acid Binding Amino Acids. Protein Science, 32(1):e4544
  22. Zhang F, Li M, Zhang J, Kurgan L, 2023. HybridRNAbind: Prediction of RNA Interacting Residues Across Structure-Annotated and Disorder-annotated Proteins. Nucleic Acids Research, 51(5):e25

    23. 2022

  23. Peng Z, Li, Z, Meng Q, ZHAO B, Kurgan L, 2022. CLIP: Accurate Prediction of Disordered Linear Interacting Peptides from Protein Sequences Using Co-evolutionary Information. Briefings in Bioinformatics, 24(1):bbac502
  24. Akbayrak IY, Caglayan SI, Kurgan L, Uversky VN, Coskuner-Weber O 2022. Insights into the structural properties of SARS-CoV-2 main protease. Current Research in Structural Biology, 4:349-355
  25. ZHAO B, Kurgan L, 2022. Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions. Biomolecules, 12(7):888
  26. BIRO B, ZHAO B, Kurgan L, 2022. Complementarity of the Residue-level Protein Function and Structure Predictions in Human Proteins. Computational and Structural Biotechnology Journal, 20:2223-2234
  27. Kurgan L, 2022. Resources for Computational Prediction of Intrinsic Disorder in Proteins. Methods, 204:132-141
  28. Chen Z, Liu X, Zhao P, Li C, Wang Y, Li F, Akutsu T, Bain C, Gasser RB, Li J, Yang Z, Gao X, Kurgan L, Jiangning J, 2022. iFeatureOmega: an Integrative Platform for Engineering, Visualization and Analysis of Features from Molecular Sequences, Structural and Ligand Data Sets. Nucleic Acids Research, 50:W434–W447
  29. ZHAO B, Kurgan L, 2022. Deep Learning in Prediction of Intrinsic Disorder in Proteins. Computational and Structural Biotechnology Journal, 20:1286-1294
  30. KATUWAWALA A, ZHAO B, Kurgan L, 2022. DisoLipPred: Accurate Prediction of Disordered Lipid Binding Residues in Protein Sequences with Deep Recurrent Networks and Transfer Learning. Bioinformatics, 38(1):115–124
  31. Zhang F, ZHAO B, Shi W, Li M, Kurgan L, 2022. DeepDISOBind: Accurate Prediction of RNA-, DNA- and Protein-binding Intrinsically Disordered Residues with Deep Multi-task Learning. Briefings in Bioinformatics, 23(1): bbab521

    32. 2021

  32. ZHAO B, Kurgan L, 2021. Surveying over 100 Predictors of Intrinsic Disorder in Proteins. Expert Review of Proteomics, 18(12):1019-1029
  33. ZHAO B, KATUWAWALA A, OLDFIELD CJ, Hu G, Wu Z, Uversky VN, Kurgan L, 2021. Intrinsic Disorder in Human RNA-binding Proteins. Journal of Molecular Biology, 433(21):167229
  34. Song R, Cao B, Peng Z, OLDFIELD CJ, Kurgan L, Wong K-C, Yang J 2021. Accurate Sequence-based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues. Biomolecules, 11(9):1337
  35. Hu G, KATUWAWALA A, Wang K, Wu Z, GHADERMARZI S, Gao J, Kurgan L, 2021. flDPnn: Accurate Intrinsic Disorder Prediction with Putative Propensities of Disorder Functions. Nature Communications, 12, 4438

    Major Highlight Winner of the CAID competition and highlighted in the commentary article A community effort to bring structure to disorder, both published in the Nature Methods journal.
    Featured among the Editor's Highlights in Structural biology, biochemistry and biophysics.
    Recommended by Faculty Opinions at https://facultyopinions.com/prime/740517747.

  36. Zhang J, GHADERMARZI S, KATUWAWALA A, Kurgan L, 2021. DNAgenie: Accurate Prediction of DNA Type Specific Binding Residues in Protein Sequences. Briefings in Bioinformatics, 22(6):bbab336
  37. GHADERMARZI S, Krawczyk K, Song J, Kurgan L, 2021. XRRpred: Accurate Predictor of Crystal Structure Quality from Protein Sequence. Bioinformatics, 37(23):4366–4374
  38. KATUWAWALA A, GHADERMARZI S, Hu G, Wu Z, Kurgan L, 2021. QUARTERplus: Accurate Disorder Predictions Integrated with Interpretable Residue-level Quality Assessment Scores. Computational and Structural Biotechnology Journal, 19:2597-2606
  39. Akbayrak IY, Caglayan SI, Durdagi S, Kurgan L, Uversky VN, Ulver B, Dervisoglu H, Haklidir M, Hasekioglu O, Coskuner-Weber O, 2021. Structures of MERS-CoV Macro Domain in Aqueous Solution with Dynamics: Impacts of Parallel Tempering Simulation Techniques and CHARMM36m and AMBER99SB Force Field Parameters. Proteins, 89(10):1289-1299
  40. Necci M, Piovesan D, CAID Predictors (including Kurgan L), DisProt Curators, Tosatto S, 2021. Critical Assessment of Protein Intrinsic Disorder Prediction. Nature Methods, 18(5):472-481
  41. ZHAO B, KATUWAWALA A, OLDFIELD CJ, Dunker AK, Faraggi E, Gsponer J, Kloczkowski A, Malhis N, Mirdita M, Obradovic Z, Söding J, Steinegger M, Zhou Y, Kurgan L, 2021. DescribePROT: Database of Amino Acid-level Protein Structure and Function Predictions. Nucleic Acids Research, 49(D1):D298–D308
  42. Chen Z, Zhao P, Li C, Li F, Xiang D, Chen Y-Z, Akutsu T, Daly RJ, Webb GI, Zhao Q, Kurgan L, Song J, 2021. iLearnPlus: A Comprehensive and Automated Machine-learning Platform for Nucleic Acid and Protein Sequence Analysis, Prediction and Visualization. Nucleic Acids Research, 49(10):e60
  43. Klionski D, Kurgan L and 2917 other co-authors, 2021. Guidelines for the Use and Interpretation of Assays for Monitoring Autophagy. Autophagy, 17(1):1-382
  44. ZHAO B, KATUWAWALA A, Uversky VN, Kurgan L, 2021. IDPology of the Living Cell: Intrinsic Disorder in the Subcellular Compartments of the Human Cell. Cellular and Molecular Life Sciences, 78:2371–2385
  45. Chen H, Li F, Wang L, Jin Y, Chi CH, Kurgan L, Song J, Shen J, 2021. Systematic Evaluation of Machine Learning Methods for Identifying Human-Pathogen Protein–Protein Interactions. Briefings in Bioinformatics, 22(3):bbaa068

    46. 2020

  46. Zhang F, Shi W, ZHANG J, Zeng M, Li M, Kurgan L, 2020. PROBselect: Accurate Prediction of Protein-binding Residues from Proteins Sequences via Dynamic Predictor Selection. Bioinformatics, 36(Supplement 2):i735–i744
  47. PENG Z, Xing Q, Kurgan L, 2020.APOD: Accurate Sequence-based Predictor of Disordered Flexible Linkers. Bioinformatics, 36(Supplement 2):i754–i761
  48. Wang K, Hu G, Wu Z, Su H, Yang J, Kurgan L, 2020. Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type.International Journal of Molecular Sciences, 21(18):6879
  49. Katuwawala A, Kurgan L, 2020. Comparative Assessment of Intrinsic Disorder Predictions with a Focus on Protein and Nucleic Acid-Binding Proteins. Biomolecules, 10(12):1636
  50. ZHANG J, GHADERMARZI S, Kurgan L, 2020. Prediction of Protein-Binding Residues: Dichotomy of Sequence-Based Methods Developed Using Structured Complexes vs. Disordered Proteins. Bioinformatics, 36(18):4729–4738
  51. KATUWAWALA A, OLDFIELD CJ, Kurgan L, 2020. Accuracy of Protein-level Disorder Predictions. Briefings in Bioinformatics, 21(5):1509-1522
  52. BARIK A, KATUWAWALA A, Hanson J, Paliwal K, Zhou Y, Kurgan L, 2020.DEPICTER: Intrinsic Disorder And Disorder Function Prediction Server. Journal of Molecular Biology, 432(11):3379-3387
  53. Gao J, Wei H, Cano A, Kurgan L, 2020. PSIONplusm Server for Accurate Multi-Label Prediction of Ion Channels and Their Types. Biomolecules, 10(6):876
  54. Li F, Chen J, Leier A, Marquez-Lago T, Liu Q, Wang Y, Revote J, Smith A, Akutsu T, Webb GI, Kurgan L, Song J, 2020. DeepCleave: A Deep Learning Predictor for Caspase and Matrix Metalloprotease Substrates and Cleavage Sites. Bioinformatics, 36(4):1057-1065
  55. Zhang J, Zeng M, Kurgan L, Wu F, Li M, 2020. NetEPD: a Network-based Essential Protein Discovery Platform. Tsinghua Science and Technology, 25(4):542-552
  56. Liu L, Kurgan L, Wu F, Wang J, 2020. Attention Convolutional Neural Network for Accurate Segmentation and Quantification of Lesions in Ischemic Stroke Disease. Medical Image Analysis, 65:101791
  57. KATUWAWALA A, OLDFIELD CJ,Kurgan L, 2020. DISOselect: Disorder Predictor Selection at the Protein Level. Protein Science, 29(1):184-200
  58. YAN J, Cheng J, Kurgan L, Uversky VN, 2020. Structural and Functional Analysis of Non-smelly Proteins. Cellular and Molecular Life Sciences, 77(12):2423–2440
  59. OLDFIELD CJ, Peng Z, Uversky VN, Kurgan L, 2020. Codon Selection Reduces GC Content Bias in Nucleic Acids Encoding for Intrinsically Disordered Proteins. Cellular and Molecular Life Sciences, 77(1):149-160
  60. WANG C, Kurgan L, 2020. Survey of Similarity-based Prediction of Drug-protein Interactions. Current Medicinal Chemistry, 27(35):5856-5886
  61. AMIRKHANI A, Kolahdoozi M, WANG C, Kurgan L, 2020. Prediction of DNA-Binding Residues in Local Segments of Protein Sequences with Fuzzy Cognitive Maps. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(4):1372-1382

    62. 2019

  62. GHADERMARZI S, Li X, Li M, Kurgan L, 2019. Sequence-derived Markers of Drug Targets and Potentially Druggable Human Proteins. Frontiers in Genetics, 10:1075
  63. Brown P, RELISH Consortium (Kurgan L and 1500 other members), Zhou Y, 2019. Large Expert-curated Database for Benchmarking Document Similarity Detection in Biomedical Literature Search. Database, 2019:baz085
  64. ZHANG J, Kurgan L, 2019. SCRIBER: Accurate and Partner Type-specific Prediction of Protein-binding Residues from Proteins Sequences. Bioinformatics, 35(14):i343–i353
  65. KATUWAWALA A, Peng Z, Yang J, Kurgan L, 2019.Computational Prediction of MoRFs, Short Disorder-to-order transitioning Protein Binding Regions. Computational and Structural Biotechnology Journal, 17:454-46
  66. Zhang F, Song H, Zeng M, Li Y, Kurgan L, Li M, 2019. DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions. Proteomics, 19(12):e1900019
  67. WANG C, Kurgan L, 2019. Review and Comparative Assessment of Similarity-based methods for Prediction of Drug-protein Interactions in the Druggable Human Proteome.Briefings in Bioinformatics, 20(6):2066-2087
  68. Groenendyk J, FAN Z, PENG Z, Kurgan L, Michalak M, 2019. Endoplasmic Reticulum and the MicroRNA Environment in the Cardiovascular System. Canadian Journal of Physiology and Pharmacology, 97(6):515-527
  69. Gao J, Miao Z, Zhang Z, Wei H, Kurgan L, 2019. Prediction of Ion Channels and Their Types from Protein Sequences: Comprehensive Review and Comparative Assessment. Current Drug Targets, 20(5):579-592
  70. Hu G, Wu Zhonghua, Oldfield C, Wang C, Kurgan L, 2019. Quality Assessment for the Putative Intrinsic Disorder in Proteins. Bioinformatics, 35:1692-1700
  71. ZHANG J, Ma Z, Kurgan L, 2019. Comprehensive Review and Empirical Analysis of Hallmarks of DNA, RNA, and Protein Binding Residues in Protein Chains.Briefings in Bioinformatics, 20(4):1250-1268

    72. 2018

  72. Groenendyk J, Paskevicius T, Urra H, Viricel C, Wang K, Barakat K, Hetz C, Kurgan L, Agellon LB, Michalak M, 2018. Cyclosporine A Binding to COX-2 Reveals a Novel Signaling Pathway that Activates the IRE1α Unfolded Protein Response Sensor.Scientific Reports, 8:16678
  73. Hu G, Wang K, Song J, Uversky VN, Kurgan L, 2018. Taxonomic Landscape of the Dark Proteomes: Whole Proteome Scale Interplay Between Structural Darkness, Intrinsic Disorder, and Crystallization Propensity.Proteomics, 18(21-22):1800243
  74. MENG F, Kurgan L, 2018. High-throughput Prediction of Disordered Moonlighting Regions in Protein Sequences. Proteins: Structure, Function, and Bioinformatics, 86(10):1097-1110
  75. CHOWDHURY S, ZHANG J, Kurgan L, 2018. In Silico Prediction and Validation of Novel RNA Binding Proteins and Residues in the Human Proteome.Proteomics, 18(21-22):1800064
  76. MENG F, Murray G, Kurgan L, Donahue HJ, 2018. Functional and Structural Characterization of Osteocytic MLO-Y 4 Cell Proteins that Encode Genes Differentially Expressed in Response to Mechanical Signals in Vitro.Scientific Reports, 8:6716
  77. Gao J, Wu Z, Hu G, Wang K, Song J, Joachimiak A, Kurgan L, 2018. Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures.Current Protein and Peptide Science, 19(20):200-210
  78. ZHANG J, Kurgan L, 2018. Review and Comparative Assessment of Sequence-based Predictors of Protein-binding Residues.Briefings in Bioinformatics, 19(5):821–837
  79. Wang H, Feng L, Webb G, Kurgan L, Song J, Lin D, 2018. Critical Evaluation of Bioinformatics Tools for the Prediction of Protein Crystallization Propensity.Briefings in Bioinformatics, 19(5):838–852

    80. 2017

  80. Hu G, Wu, Z, Uversky V, Kurgan L, 2017. Functional Analysis of Human Hub Proteins and Their Interactors Involved in the Intrinsic Disorder-enriched Interactions.International Journal of Molecular Sciences, 18(12):2761
  81. MENG F, CHEN W, Kurgan L, 2017. fDETECT Webserver: Fast Predictor of Propensity for Protein Production, Purification, and Crystallization.BMC Bioinformatics, 18(1):580
  82. Ferreira L, Wu Z, Kurgan L, Uversky VN, Zaslavsky BY, 2017. How to Manipulate Partition Behavior of Proteins in Aqueous Two-phase Systems: Effect of Trimethylamine N-oxide (TMAO). Fluid Phase Equilibria, 449:217-224
  83. MENG F, Uversky VN, Kurgan L, 2017. Comprehensive Review of Methods for Prediction of Intrinsic Disorder and its Molecular Functions. Cellular and Molecular Life Sciences, 74(17):3069-3090
  84. YAN J, Kurgan L, 2017. DRNApred, Fast Sequence-based Method that Accurately Predicts and Discriminates DNA- and RNA-binding Residues.Nucleic Acids Research, 45(10):e84
  85. WU Z, HU G, Wang K, Zaslavsky BY, Kurgan L, Uversky VN, 2017. What are the Structural Features that Drive Partitioning of Proteins in Aqueous Two-phase Systems?BBA Proteins and Proteomics, 1865(1):113-120

    86. 2016

  86. PENG Z, Uversky VN, Kurgan L, 2016. Genes Encoding Intrinsic Disorder in Eukaryota Have High GC Content.Intrinsically Disordered Proteins, 4(1):e1262225
  87. Lieutaud P, Ferron F, Uversky AV, Kurgan L, Uversky VN, Longhi S 2016. How Disordered is My Protein and What is Its Disorder For? A Guide Through the “Dark Side” of the Protein Universe. Intrinsically Disordered Proteins, 4(1):e1259708
  88. Na I, MENG F, Kurgan L, Uversky VN, 2016. Autophagy-related Intrinsically Disordered Proteins in Intra-nuclear Compartments. Molecular BioSystems, 12:2798-2817
  89. MENG F,Kurgan L, 2016. DFLpred: High Throughput Prediction of Disordered Flexible Linker Regions in Protein sequences. Bioinformatics, 32(12):i341-i350
  90. WANG C, Uversky VN, Kurgan L, 2016. Disordered Nucleiome: Abundance of Intrinsic Disorder in the DNA- and RNA-binding Proteins in 1121 Species from Eukaryota, Bacteria and Archaea. Proteomics, 16(10):1486-1498
  91. YAN J, Dunker AK, Uversky VN, Kurgan L, 2016. Molecular Recognition Features (MoRFs) in Three Domains of Life (COVER STORY). Molecular BioSystems, 12:697-710
  92. MENG F, Na I, Kurgan L, Uversky VN, 2016. Compartmentalization and Functionality of Nuclear Disorder: Intrinsic Disorder and Protein-Protein Interactions in Intra-Nuclear Compartments. International Journal of Molecular Sciences, 17(1): article 24
  93. WANG C, HU G, Wang K, Brylinski M, Xie L, Kurgan L, 2016. PDID: Database of Molecular-level Putative Protein-drug Interactions in the Structural Human Proteome. Bioinformatics, 32(4):579-586
  94. YAN J, Friedrich S, Kurgan L, 2016. A Comprehensive Comparative Review of Sequence Based Pedictors of DNA and RNA Binding Residues. Briefings in Bioinformatics, 17(1):88-105
  95. Gao J, Cui W, Sheng Y, Jishou J, Kurgan L, 2016. PSIONplus: Accurate Sequence-Based Predictor of Ion Channels and Their Types. PLoS ONE, 11(4):e0152964
  96. HU G, WU Z, Wang K, Uversky VN, Kurgan L, 2016. Untapped Potential of Disordered Proteins in Current Druggable Human Proteome. Current Drug Targets, 17(10):1198-1205

    97. 2015

  97. PENG Z, Kurgan L, 2015. High-throughput Prediction of RNA, DNA, AND Protein Binding Regions Mediated by Intrinsic Disorder. Nucleic Acids Research, 43(18):e121
  98. WU Z, HU G, Yang J, PENG Z, Uversky VN, Kurgan L, 2015. In Various Protein Complexes, Disordered Protomers Have Large Per-residue Surface Areas and Area of Protein-, DNA- and RNA Binding Interfaces. FEBS Letters, 589(19A):2561-2569
  99. MENG F, Badierah R, Almehdar H, Redwan E Kurgan L, Uversky VN, 2015. Unstructural Biology of the Dengue Virus Proteins. FEBS Journal, 282(17):3368-3394
  100. FAN X, Kurgan L, 2015. Comprehensive Overview and Assessment of Computational Prediction of MicroRNA Targets in Animals. Briefings in Bioinformatics16(5):780-794
  101. PENG Z, YAN J, FAN X, MIZIANTY MJ, Xue B, Wang K, HU G, Uversky VN, Kurgan L, 2015. Exceptionally Abundant Exceptions: Comprehensive Characterization of Intrinsic Disorder in all Domains of Life. Cellular and Molecular Life Sciences, 72(1):137-151
  102. Ferreira L, FAN X, Madeira P, Kurgan L, Uversky VN,Zaslavsky BY, 2015. Analyzing the Effects of Protecting Osmolytes on Solute-Water Interactions by Solvatochromic Comparison Method: II. Globular Proteins. RSC Advances, 5:59780-59791
  103. CHEN K, Wang D, Kurgan L, 2015. Systematic Investigation of Sequence and Structural Motifs that Recognize ATP. Computational Biology and Chemistry, 56:131-141

    104. 2014

  104. Groenendyk J, FAN X, PENG Z, Ilnytskyy Y, Kurgan L, Michalak M, 2014. Genome-wide Analysis of Thapsigargin-induced MicroRNAs and Their Targets in NIH3T3 Cells. Genomics Data, 2:325-327
  105. MIZIANTY MJ, FAN X, YAN J, Chalmers E, Woloschuk C, Joachimiak A, Kurgan L, 2014. Covering the Complete Proteomes with X-ray Structures - a Current Snapshot. Acta Crystallographica Section D, D70:2781-2793
  106. HU G, Wang K, Groenendyk J, Barakat K, MIZIANTY MJ, Ruan J, Michalak M, Kurgan L, 2014. Human Structural Proteome-wide Characterization of Cyclosporine A Targets. Bioinformatics, 30(24):3561-3566
  107. ZHANG H, Kurgan L, 2014. Improved Prediction of Residue Flexibility by Embedding Optimized Amino Acid Grouping into RSA-based Linear Models. Amino Acids, 46(12):2665-2680
  108. Groenendyk J, PENG Z, Dudek E, FAN X, MIZIANTY MJ, Dufey E, Erra H, Sepulveda D, Rojas-Rivera D, Lim Y, Kim DH, Baretta K, Srikanth S, Gwack Y, Ahnn J, Kaufman RJ, Lee S-K, Hetz C, Kurgan L, Michalak M, 2014. Interplay Between the Oxidoreductase PDIA6 and microRNA-322 Controls the Response to Disrupted Endoplasmic Reticulum Calcium Homeostasis (COVER STORY). Science Signaling, 7(329:ra54)
  109. Fuxreiter M, Toth-Petroczy A, Kraut DA, Matouschek AT, Lim RYH, Xue B, Kurgan L, Uversky VN, 2014. Disordered Proteinaceous Machines. Chemical Reviews, 114(13):6806-6843
  110. Xue B, Blocquel D, Habchi J, Uversky AV, Kurgan L, Uversky VN, Longhi S, 2014. Structural Disorder in Viral Proteins. Chemical Reviews, 114(13):6880-6911
  111. FAN X, Xue B, Dolan PT, LaCount DJ, Kurgan L, Uversky VN, 2014. The Intrinsic Disorder Status of the Human Hepatitis C Virus Proteome. Molecular BioSystems, 10:1345-1363
  112. PENG Z, Sakai Y, Kurgan L, Sokolowski B, Uversky VN, 2014. Intrinsic Disorder in the BK Channel and its Interactome. PLoS ONE, 9(4):e94331
  113. Ferreira L, FAN X, Mikheeva LM, Madeira PP, Kurgan L, Uversky VN, Zaslavsky BY, 2014. Structural Features Important for Differences in Protein Partitioning in Aqueous Dextran-polyethylene Glycol Two-phase Systems of Different Ionic Composition. BBA Proteins and Proteomics, 1844(3):694-704
  114. ZHANG H, Kurgan L, 2014. Sequence-based Gaussian Network Model for Protein Dynamics. Bioinformatics, 30(4):497-505
  115. PENG Z, MIZIANTY MJ, Kurgan L, 2014. Genome-scale Prediction of Proteins with Long Intrinsically Disordered Regions. Proteins: Structure, Function, and Bioinformatics, 82(1):145-158
  116. FAN X, Kurgan L, 2014. Accurate Prediction of Disorder in Protein Chains with a Comprehensive and Empirically Designed Consensus. Journal of Biomolecular Structure and Dynamics, 32(3):448-464
  117. KEDARISETTI P, MIZIANTY MJ, Kaas Q, Craik D, Kurgan L, 2014. Prediction and Characterization of Cyclic Proteins from Sequences in Three Domains of Life. BBA Proteins and Proteomics, 1844:181-190
  118. PENG Z, Oldfiel CJ, Xue B, MIZIANTY MJ, Dunker AK, Kurgan L, Uversky VN, 2014. A Creature with a Hundred of Waggly Tails: Intrinsically Disordered Proteins in the Ribosome. Cellular and Molecular Life Sciences, 71(8):1477-1504
  119. YAN J, MARCUS M, Kurgan L, 2014. Comprehensively Designed Consensus of Standalone Secondary Structure Predictors Improves Q3 by Over 3%. Journal of Biomolecular Structure and Dynamics, 32(1):36-51

    120. 2013

  120. Uversky AV, Xue B, PENG Z, Kurgan L, Uversky VN, 2013. On the Intrinsic Disorder Status of the Major Players in Programmed Cell Death Pathways. F1000Research, 2:190
  121. PENG Z, Xue B, Kurgan L, Uversky VN, 2013. Resilience of Death: Intrinsic Disorder in Proteins Involved in the Programmed Cell Death. Cell Death and Differentiation, 20:1257-1267
  122. YAN J, MIZIANTY MJ, FILIPOW P, Uversky VN, Kurgan L, 2013. RAPID: Fast and Accurate Sequence-based Prediction of Intrinsic Disorder Content on Proteomic Scale. BBA Proteins and Proteomics, 1834(8):1671-1680
  123. Xue B, Romero P, Noutsou M, Maurice MM, Rudiger S, William Jr. AM, MIZIANTY MJ, Kurgan L, Uversky VN, Dunker AK, 2013. Stochastic Machines as a Colocalization Mechanism for Scaffold Protein Function. FEBS Letters, 587(11):1587-1591
  124. MIZIANTY MJ, PENG Z, Kurgan L, 2013. MFDp2 - Accurate Predictor of Disorder in Proteins by Fusion of Disorder Probabilities, Content and Profiles. Intrinsically Disordered Proteins, 1:e24428
  125. Durand M, Komarova S, Bhargava A, Li K, Fiorino K, Maria O, Nabavi N, Monolson MF, Harrison R, Dixon J, Sims SM, MIZIANTY MJ, Kurgan L, Haroun S, Boire G, Lucena-Fernandes M, de Brum-Fernandes AJ, 2013. Monocytes from Patients with Osteoarthritis Display Increased Osteoclastogenesis and Bone Resorption: The In Vitro Osteoclast Differentiation in Arthritis (IODA) Study. Arthritis and Rheumatism, 65(1):148-158
  126. Nguyen D.T., Nguyen C.D., Hargraves R., Kurgan L, Cios KJ, 2013. mi-DS: Multiple-Instance Learning Algorithm. IEEE Transactions on Cybernetics, 43(1):143-154

    127. 2012

  127. Oates ME, Romero P, Wang K, Ishida T, Ghalwash M, MIZIANTY MJ, Xue B, Dosztanyi Z, Uversky V, Obradovic Z, Kurgan L, Dunker AK, Gough J, 2012. D2P2: Database of Disordered Protein Predictions. Nucleic Acids Research, 41:D508-516
  128. HU G, Gao J, Wang K, MIZIANTY MJ, Ruan J, Kurgan L, 2012. Finding Protein Targets for Small Biologically Relevant Ligands Across Fold Space Using Inverse Ligand Binding Predictions. Structure, 20:1815-1822
  129. Howell M, Green R, Killeen A, Wedderburn L, Picascio V, Alejandro A, PENG Z, Larina M, Xue B, Kurgan L, Uversky V, 2012. Not that Rigid Midgets and not so Flexible Giants: On the Abundance and Roles of Intrinsic Disorder in Short and Long Proteins. Journal of Biological Systems, 20(4):471-511
  130. MIRI DISFANI F, Hsu W-L, MIZIANTY MJ, Oldfield CJ, Xue B, Dunker AK, Uversky VN, Kurgan L, 2012. MoRFpred, a Computational Tool for Sequence-based Prediction and Characterization of Short Disorder-to-order Transitioning Binding Regions in Proteins. Bioinformatics, 28(12):i75-i83
  131. Gao J, Eshel E, Zhou Y, Jishou J, Kurgan L, 2012. BEST: improved prediction of B-cell epitopes from antigen sequences. PLoS ONE, 7(6):e40104
  132. PENG Z-L, MIZIANTY MJ, Xue B, Kurgan L, Uversky VN, 2012. More Than Just Tails: Intrinsic Disorder in Histone Proteins. Molecular BioSystems, 8:1886-1901
  133. Xue B, MIZIANTY MJ, Kurgan L, Uversky VN, 2012. Protein Intrinsic Disorder as Flexible Armor and Weapon of HIV-1. Cellular and Molecular Life Sciences, 69:1211-1259
  134. CHEN K, MIZIANTY MJ, Kurgan L, 2012. Prediction and Analysis of Nucleotide Binding Residues Using Sequence and Sequence-derived Structural Descriptors. Bioinformatics, 28(3):331-341
  135. MIZIANTY MJ, Kurgan L, 2012. CRYSpred: Accurate Sequence-Based Protein Crystallization Propensity Prediction Using Sequence-Derived Structural Characteristics. Protein and Peptide Letters, 19(1):40-49
  136. ZHANG H, ZHANG T, Gao J, Ruan J, Shen S, Kurgan L, 2012. Determination of Protein Folding Kinetic Types Using Sequence and Predicted Secondary Structure and Solvent Accessibility. Amino Acids, 42:271-283
  137. Faraggi E, ZHANG T, Yang Y, Kurgan L, Zhou Y, 2012. SPINE X: Improving Protein Secondary Structure Prediction by Multi-step Learning Coupled with Prediction of Solvent Accessible Surface Area and Backbone Torsion Angles. Journal of Computational Chemistry, 33(3):259-267
  138. PENG Z-L, Kurgan L, 2012. Comprehensive Comparative Assessment of In-silico Predictors of Disordered Regions. Current Protein and Peptide Science, special issue on Intrinsically Disordered Proteins, 13:6-18
  139. STACH W, Pedrycz W, Kurgan L, 2012. Learning of Fuzzy Cognitive Maps Using Density Estimate. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 42(3):900-912

    140. 2011

  140. MIZIANTY MJ, Kurgan L, 2011. Sequence-based Prediction of Protein Crystallization, Purification, and Production Propensity. Bioinformatics, 27(13):i24-i33
  141. MIZIANTY MJ, ZHANG T, Xue B, Zhou Y, Dunker AK, Uversky VN, Kurgan L, 2011. In-silico prediction of disorder content using hybrid sequence representation. BMC Bioinformatics, 12:245
  142. CHEN K, MIZIANTY MJ, Gao J, Kurgan L, 2011. A Critical Comparative Assessment of Predictions of Protein Binding Sites for Biologically Relevant Organic Compounds. Structure, 19(5):613-621
  143. CHEN K, MIZIANTY MJ, Kurgan L, 2011. ATPsite: Sequence-based Prediction of ATP-binding Residues. Proteome Science, 9(Suppl. 1):S4
  144. ZHANG H, ZHANG T, CHEN K, KEDARISETTI KD, MIZIANTY MJ, Bao Q, STACH W, Kurgan L, 2011. Critical Assessment of High-throughput Standalone Methods for Secondary Structure Prediction. Briefings in Bioinformatics, 12(6):672-688
  145. KEDARISETTI KD, MIZIANTY MJ, Dick S, Kurgan L, 2011. Improved Sequence-based Prediction of Strand Residues. Journal of Bioinformatics and Computational Biology, 9(1):67-89
  146. MIZIANTY MJ, Kurgan L, 2011. Improved Identification of Outer Membrane Beta Barrel Proteins Using Primary Sequence, Predicted Secondary Structure and Evolutionary Information. Proteins: Structure, Function, and Bioinformatics, 79(1):294-303
  147. Durand M, Boire G, Komarova S, Dixon J, Sims SM, Harrison R, Nabavi N, Maria O, Monolson MF, MIZIANTY MJ, Kurgan L, de Brum-Fernandes AJ, 2011. The Increased In Vitro Osteoclastogenesis in Patients with Rheumatoid Arthritis is Due to Increased Percentage of Precursors and Decreased Apoptosis - The In Vitro Osteoclast Differentiation in Arthritis (Ioda) Study. Bone, 48:588-596
  148. CHEN K, STACH W, HOMAEIAN L, Kurgan L, 2011. iFC2: An Integrated Web-server for Improved Prediction of Protein Structural Class, Fold Type, and Secondary Structure Content. Amino Acids, 40(3):963-973
  149. Kurgan L, MIRI DISFANI F, 2011. Structural Protein Descriptors in 1-Dimension and Their Sequence-Based Predictions. Current Protein and Peptide Science, special issue on Machine Learning Models in Protein Bioinformatics, 12(6):470-489

    150. 2010

  150. MIZIANTY MJ, STACH W, CHEN K, KEDARISETTI KD, MIRI DISFANI F, Kurgan L, 2010. Improved Sequence-based Prediction of Disordered Regions with Multilayer Fusion of Multiple Information Sources. Bioinformatics, 26(18):i489-i496
  151. Gao J, ZHANG T, ZHANG H, Shen S, Ruan J, Kurgan L, 2010. Accurate Prediction of Protein Folding Rates from Sequence and Sequence-derived Residue Flexibility and Solvent Accessibility. Proteins: Structure, Function, and Bioinformatics, 78(9):2114-2130
  152. ZHANG T, ZHANG H, CHEN K, Ruan J, Shen S, Kurgan L, 2010. Analysis and Prediction of RNA-Binding Residues Using Sequence, Evolutionary Conservation, and Predicted Secondary Structure and Solvent Accessibility. Current Protein and Peptide Science, special issue on Protein Folding, Stability and Interactions, 11(7):609-628
  153. Shin J-H, Smith D, Swiercz W, Staley K, Rickard K, Montero F, Kurgan L, Cios K, 2010. Recognition of Partially Occluded and Rotated Images with a Network of Spiking Neurons. IEEE Transactions on Neural Networks, 21(11):1697-1709
  154. STACH W, Kurgan L, Pedrycz W, 2010. A Divide and Conquer Method for Learning Large Fuzzy Cognitive Maps. Fuzzy Sets and Systems, 161(19):2515-2532
  155. BASS SD, Kurgan L, 2010. Discovery of Factors Influencing Patent Value Based on Machine Learning in Patents in the Field of Nanotechnology. Scientometrics, 82(2):217-241
  156. MIZIANTY MJ, Kurgan L, Ogiela M, 2010. Discretization as the Enabling Technique for the Naive Bayes and Semi-Naive Bayes Based Classification. Knowledge Engineering Review, 25(4):421-449

    157. 2009

  157. MIZIANTY MJ, Kurgan L, 2009. Modular Prediction of Protein Structural Classes from Sequences of Twilight-Zone Identity with Predicting Sequences. BMC Bioinformatics, 10:414
  158. MIZIANTY MJ, Kurgan L, 2009. Meta Prediction of Protein Crystallization Propensity. Biochemical and Biophysical Research Communications, 390(1):10-15
  159. Kurgan L, MIZIANTY MJ, 2009. Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis. Natural Science, 1(2):93-106
  160. Kurgan L, Razib AA, Aghakhani S, Dick S, MIZIANTY MJ, Jahandideh S, 2009. CRYSTALP2: Sequence-based Protein Crystallization Propensity Prediction. BMC Structural Biology, 9:50
  161. CHEN K, Kurgan L, 2009. Investigation of Atomic Level Patterns in Protein - Small Ligand Interactions. PLoS ONE, 4(2):e4473
  162. ZHANG H, ZHANG T, CHEN K, Shen S, Ruan J, Kurgan L, 2009. On the Relation between Residue Flexibility and Local Solvent Accessibility in Proteins. Proteins: Structure, Function, and Bioinformatics, 76(3):617-636
  163. JIANG Y, Iglinski P, Kurgan L, 2009. Prediction of Protein Folding Rates from Primary Sequences using Hybrid Sequence Representation. Journal of Computational Chemistry, 30(5):772-783
  164. CHEN K, JIANG Y, Du L, Kurgan L, 2009. Prediction of Integral Membrane Protein Type by Collocated Hydrophobic Amino Acid Pairs. Journal of Computational Chemistry, 30(1):163-172

    165. 2008

  165. Gehrke AS, Sun S, Kurgan L, 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
  166. ZHENG C, Kurgan L, 2008. Prediction of ß-turns at Over 80% Accuracy Based on an Ensemble of Predicted Secondary Structures and Multiple Alignments. BMC Bioinformatics, 9:430
  167. ZHANG T, ZHANG H, CHEN K, Shen S, Ruan J, Kurgan L, 2008. Accurate Sequence-based Prediction of Catalytic Residues. Bioinformatics, 24(20):2329-2338
  168. Ruan J, Chen H, Kurgan L, CHEN K, Kang C, Pu P, 2008. HuMiTar: A sequence-based Method for Prediction of Human microRNA Targets. Algorithms for Molecular Biology, 3:16
  169. ZHANG H, ZHANG T, CHEN K, Shen S, Ruan J, Kurgan L, 2008. Sequence Based Residue Depth Prediction Using Evolutionary Information and Predicted Secondary Structure. BMC Bioinformatics, 9:388
  170. CHEN K, Huzil T, Friedman H, Ramachandran P, Antoniou A, Tuszynski J, Kurgan L, 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
  171. Kurgan L, 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
  172. CAMPBELL K, Kurgan L, 2008. Sequence-only Based Prediction of ß-turn Location and Type Using Collocation of Amino Acid Pairs. Open Bioinformatics Journal, 2:37-49
  173. Kurgan L, 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
  174. FARHANGFAR A, Kurgan L, Dy J, 2008. Impact of Imputation of Missing Values on Classification Error for Discrete Data. Pattern Recognition, 41(12):3692-3705
  175. CHEN K, Kurgan M, Kurgan L, 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
  176. Kurgan L, ZHANG T, ZHANG H, Shen S, Ruan J, 2008. Secondary Structure Based Assignment of the Protein Structural Classes. Amino Acids, 35(3):551-564
  177. KEDARISETTI K, Dick S, Kurgan L, 2008. Searching for Factors that Distinguish Disease-prone and Disease-resistant Prions via Sequence Analysis. Bioinformatics and Biology Insights, 2:133-144
  178. Golmohammadi SK, Kurgan L, 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
  179. CHEN K, Kurgan L, Ruan J, 2008. Prediction of Protein Structural Class Using Novel Evolutionary Collocation Based Sequence Representation. Journal of Computational Chemistry, 29(10):1596-1604
  180. Kurgan L, 2008. On the Relation between the Predicted Secondary Structure and the Protein Size. Protein Journal, 24(4):234-239
  181. STACH W, Kurgan L, 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
  182. RAK R, Kurgan L, 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

    183. 2007

  183. CHEN K, Kurgan L, 2007. PFRES: Protein Fold Classification by Using Evolutionary Information and Predicted Secondary Structure. Bioinformatics, 23(21):2843-2850
  184. CHEN K, Kurgan L, Ruan J, 2007. Prediction of Flexible/Rigid Regions in Proteins from Sequences Using Collocated Amino Acid Pairs. BMC Structural Biology, 7:25
  185. Kurgan L, CHEN K, 2007. Prediction of Protein Structural Class for the Twilight Zone Sequences. Biochemical and Biophysical Research Communications, 357(2):453-460
  186. RAK R, Kurgan L, Reformat M, 2007. xGENIA: A comprehensive OWL ontology based on the GENIA corpus. Bioinformation, 1(9):360-362
  187. HOMAEIAN L, Kurgan L, 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
  188. CHEN K, Kurgan L, RAHBARI M, 2007. Prediction of Protein Crystallization Using Collocation of AA pairs. Biochemical and Biophysical Research Communications, 355(3):764-769
  189. Huzil T, CHEN K, Kurgan L, Tuszynski J, 2007. The Roles of Beta-Tubulin Mutations and Isotype Expression in Acquired Drug Resistance. Cancer Informatics, 3:159-181
  190. RAK R, Kurgan L, 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
  191. Kurgan L, STACH W, Ruan J, 2007. Novel Scales Based on Hydrophobicity Indices for Secondary Protein Structure. Journal of Theoretical Biology, 248(2):354-366
  192. FARHANGFAR A, Kurgan L, 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

    193. 2006

  193. Kurgan L, 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
  194. KEDARISETTI K, Kurgan L, Dick S, 2006. Classifier Ensembles for Protein Structural Class Prediction with Varying Homology. Biochemical and Biophysical Research Communications, 348(3):981-988
  195. KEDARISETTI K, Kurgan L, 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
  196. Ruan J, CHEN K, Tuszynski J, Kurgan L, 2006. Quantitative Analysis of the Conservation of the Tertiary Structure of Protein Segments. Protein Journal, 25(5):301-315
  197. CHEN K, Ruan J, Kurgan L, 2006. Prediction of Three Dimensional Structure of Calmodulin. Protein Journal, 25(1):57-70
  198. Kurgan L, 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
  199. Swiercz W, Cios KJ, Staley K, Kurgan L, Accurso F, Sagel S, 2006. A New Synaptic Plasticity Rule for Networks of Spiking Neurons. IEEE Transactions on Neural Networks, 17(1):94-105
  200. Kurgan L, Musilek P, 2006. A Survey of Knowledge Discovery and Data Mining Process Models. Knowledge Engineering Review, 21(1):1-24
  201. Kurgan L, 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

    202. 2005

  202. Ruan J, WANG K, Yang J, Kurgan L, 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
  203. STACH W, Kurgan L, Pedrycz W, Reformat M, 2005. Genetic Learning of Fuzzy Cognitive Maps. Fuzzy Sets and Systems, 153(3):371-401

    204. 2004

  204. STACH W, Kurgan L, Pedrycz W, Reformat M, 2004. Learning Fuzzy Cognitive Maps with Required Precision using Genetic Algorithm Approach. Electronics Letters, 40(24):1519-1520
  205. Kurgan L, Cios KJ, 2004. CAIM Discretization Algorithm. IEEE Transactions on Data and Knowledge Engineering, 16(2):145-153
  206. Cios KJ, Kurgan L, 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

    207. 2001

  207. Kurgan L, 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

Books

  1. Kloczkowski A, Kurgan L, Faraggi E, 2024. Prediction of Protein Secondary Structure (Methods in Molecular Biology, volume 2867) , Springer, ISBN 978-1-0716-4195-8
  2. Kurgan L, 2023. Machine Learning in Bioinformatics of Protein Sequences, World Scientific, ISBN 978-981-125-857-2
  3. Cios K, Pedrycz W, Swiniarski R, Kurgan L, 2007. Data Mining: A Knowledge Discovery Approach, Springer, ISBN 0-387-33333-9

Edited Volumes and Special Issues

  1. Wu F-X, Li M, Kurgan L, Rueda L, 2022. Guest Editorial: Deep Neural Networks for Precision Medicine. Neurocomputing, 469:330-331
  2. Chen Y, Dougherty E, Huang Y, Kurgan L, Luo F, Kloczkowski A, Li Y, (Eds.) 2021. Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE Press, ISBN 978-1-6654-0126-5, 3970 pages
  3. Kurgan L, Zhou Y, (Eds.) 2011. Editorial for special issue on Machine Learning Models in Protein Bioinformatics, Current Protein and Peptide Science, 12(6):455, Bentham Science Publishers
  4. Wani A, Kantardzic M, Palade V, Kurgan L, Qi A, (Eds.) 2009. Proceedings of the Eight International Conference on Machine Learning and Applications (ICMLA'09), IEEE Press, ISBN 978-0-7695-3926-3, 812 pages
  5. Wani A, Chen X, Casasent D, Kurgan L, 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
  6. Wani A, Kantardzic M, Li T, Liu Y, Kurgan L, 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
  7. Kurgan L, Reformat M, Cios KJ, (Eds.) 2007. Editorial for special issue on Machine Learning in the Life Sciences, IEEE Engineering in Medicine and Biology Magazine, 26(2):14-16, IEEE Press
  8. Reformat M, Kurgan L, (Eds.) 2007. Proceedings of the Human Centric Computing and Data Processing (HC2DP 2007) Symposium 25 pages
  9. Wani A, Li T, Kurgan L, 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
  10. Musilek P, Reformat M, Kurgan L, (Eds.) 2005. Editorial for special issue on Biologically Inspired Computing and Computers in Biology, Neural Networks World, 15(3):187
  11. Wani A, Milanowa M, Kurgan L, 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
  12. Dick S, Kurgan L, 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
  13. Kurgan L, Musilek P, Pedrycz W, Reformat M, (Eds.) 2005. Proceedings of the Human Centric Computing (HC2 2005) Symposium 71 pages

Book Chapters

  1. Uversky V, Kurgan L, 2023. Overview Update: Computational Prediction of Intrinsic Disorder in Proteins, In: Heath R, (Ed.), Current Protocols, 3:e802. doi: 10.1002/cpz1.802, Wiley (ISSN: 2691-1299)
  2. BI Z, Kurgan L, 2023. Databases of Protein Structure and Function Predictions at the Amino Acid Level, In: Kurgan L, (Ed.) Machine Learning in Bioinformatics of Protein Sequences, 329-354, World Scientific (ISBN 978-981-125-857-2)
  3. BI Z, Kurgan L, 2023. Machine Learning for Intrinsic Disorder Prediction, In: Kurgan L, (Ed.) Machine Learning in Bioinformatics of Protein Sequences, 205-236, World Scientific (ISBN 978-981-125-857-2)
  4. Chen Z, Li F, Wang X, Wang Y, Kurgan L, Song J, 2023. Designing Effective Predictors of Protein Post-translational Modifications using iLearnPlus, In: Kurgan L, (Ed.) Machine Learning in Bioinformatics of Protein Sequences, 309-328, World Scientific (ISBN 978-981-125-857-2)
  5. Li M, Zhang F, Kurgan L, 2023. Machine Learning Methods for Predicting Protein-nucleic acids Interactions, In: Kurgan L, (Ed.) Machine Learning in Bioinformatics of Protein Sequences, 265-288, World Scientific (ISBN 978-981-125-857-2)
  6. Wu Z, Hu G, OLDFIELD C, Kurgan L, 2020. Prediction of Intrinsic Disorder with Quality Assessment Using QUARTER, In: Kihara D, (Ed.), Methods in Molecular Biology, 2165:83-101, Springer Nature (ISBN 978-1-0716-0707-7)
  7. OLDFIELD C, FAN X, WANG, C, Dunker KA, Kurgan L, 2020. Computational Prediction of Intrinsic Disorder in Protein Sequences with the disCoP Meta-predictor, In: Kragelund B and Skriver K (Eds), Methods in Molecular Biology, 2141:21-35, Springer Nature (ISBN 978-1-0716-0523-3)
  8. BARIK A, Kurgan L, 2020. A Comprehensive Overview of Sequence-Based Protein-Binding Residue Predictions for Structured and Disordered Regions, In: Gromiha M, (Ed.), Protein Interactions: Computational Methods, Analysis and Applications, 33-58, World Scientific (ISBN: 978-981-121-187-4)
  9. Kurgan L, Li M, Li Y, 2019. The Methods and Tools for Intrinsic Disorder Prediction and Their Application to Systems Medicine, In: Wolkenhauer O, (Ed.), Systems Medicine: Integrative Qualitative and Computational Approaches, Elsevier, (ISBN 978-0-12-801238-3)
  10. KATUWAWALA A, GHADERMARZI S, Kurgan L, 2019. Computational Prediction of Functions of Intrinsically Disordered Regions, In: Uversky VN, (Ed.), Dancing Protein Clouds: Intrinsically Disordered Proteins in Health and Disease, Part A, 341-369, Elsevier (ISBN: 978-0-12-816851-6)
  11. OLDFIELD C, Uversky VN, Dunker AK, Kurgan L, 2019. Introduction to Intrinsically Disordered Proteins and Regions, In: Salvi N, (Ed.), Intrinsically Disordered Proteins: Dynamics, Binding and Function, 1-36, Elsevier (ISBN 978-0-12-816348-1)
  12. OLDFIELD C, PENG Z, Kurgan L, 2019. Disordered RNA Binding Region Prediction with DisoRDPbind, In: Heise T, (Ed.), Methods in Molecular Biology, 2106:225-239, Springer Nature (ISBN 978-1-07-160231-7)
  13. WANG C, Brylinski M, Kurgan L, 2019. PDID: Database of Experimental and Putative Drug Targets in Human Proteome, In: Roy K, (Ed.), In Silico Drug Design: Repurposing Techniques and Methodologies, 827-847, Elsevier (ISBN 978-0-12-816125-8)
  14. OLDFIELD C, CHEN K, Kurgan L, 2019. Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences, In: Kister A, (Ed.), Methods in Molecular Biology, 1958:73-100, Springer Nature (ISBN 978-1-4939-9161-7)
  15. OLDFIELD C, Uversky VN, Kurgan L, 2019. Predicting Functions of Disordered Proteins with MoRFpred, In: Sikosek T, (Ed.), Methods in Molecular Biology, 1851:337-352, Springer Nature (ISBN 978-1-4939-8735-1)
  16. Hu G, Kurgan L, 2018. Sequence Similarity Searching, In: Dunn BM, (Ed.), Current Protocols in Protein Science, e71, doi: 10.1002/cpps.71, Wiley (ISBN 978-0-4711-4086-3)
  17. MENG F, Uversky VN, Kurgan L, 2017. Computational Prediction of Intrinsic Disorder in Proteins, In: Dunn BM, (Ed.), Current Protocols in Protein Science, 88:2.16.1–2.16.14, Wiley (ISBN 978-0-4711-4086-3)
  18. PENG Z, WANG C, Uversky VN, Kurgan L, 2017. Prediction of Disordered RNA, DNA, and Protein Binding Regions Using DisoRDPbind, In: Zhou Y, Kloczkowski A, Faraggi E, Yang Y, (Eds.), Methods in Molecular Biology, 1484:187-203, Humana Press (ISBN 978-1-4939-6404-8)
  19. MENG F, Kurgan L, 2016. Computational Prediction of Protein Secondary Structure from Sequence, In: Dunn BM, (Ed.), Current Protocols in Protein Science, 86:2.3.1-2.3.10, Wiley (ISBN 978-0-4711-4086-3)
  20. Gao J, Kurgan L, 2014. Computational Prediction of B-cell Epitopes from Antigen Sequences, In: De RK, Tomar T, (Eds.), Methods in Molecular Biology, 1184:197-216, Humana Press (ISBN 978-1-4939-1114-1)
  21. MIZIANTY MJ, Uversky V, Kurgan L, 2014. Prediction of Intrinsic Disorder in Proteins using MFDp2, In: Kihara D, (Ed.), Methods in Molecular Biology, 1137:147-162, Humana Press (ISBN 978-1-4939-0365-8)
  22. CHEN K, Kurgan L, 2013. Computational Prediction of Secondary and Supersecondary Structures, In: Kister A, (Ed.), Methods in Molecular Biology, 932:63-86, Humana Press (ISBN 978-1-62703-064-9)
  23. CHEN K, Kurgan L, 2012. Neural Networks in Bioinformatics, In: Rozenberg R, Baeck T, Kok J, (Eds.), Handbook of Natural Computing, 565-583, Springer (ISBN 978-3-540-92909-3)
  24. STACH W, Kurgan L, Pedrycz W, 2010. Expert-Based and Computational Methods for Developing Fuzzy Cognitive Maps, In: Glykas M, (Ed.), Advances in Theory, Methodologies, Tools and Applications, Studies in Fuzziness and Soft Computing, vol. 247, 23-41, Springer (ISBN 978-3-642-03219-6)
  25. Cios KJ, Kurgan L, 2010. Machine Learning Algorithms Inspired by the work of Ryszard Spencer Michalski, In: Koronacki J, Ras ZW, Wierzchon ST, Kacprzyk J, (Ed.), Advances in Machine Learning I (In memoriam of Prof. Ryszard S. Michalski), Studies in Computational Intelligence vol. 262, 49-74, Springer (ISBN 978-3-642-05176-0)
  26. STACH W, Kurgan L, Pedrycz W, 2005. A Survey of Fuzzy Cognitive Map Learning Methods, In: Grzegorzewski P, Krawczak M, Zadrozny S, (Ed.), Issues in Soft Computing: Theory and Applications, 71-84, Exit (ISBN 83-87674-98-2)
  27. Kurgan L, Cios KJ, Sontag M, Accurso F, 2005. Mining the Cystic Fibrosis Data, In: Zurada J, Kantardzic M, (Ed.), Next Generation of Data-Mining Applications, 415-444, IEEE Press - Wiley (ISBN 0-471-65605-4)
  28. Cios KJ, Kurgan L, 2005. Trends in Data Mining and Knowledge Discovery, In: Pal NR, Jain LC, (Ed.), Advanced Techniques in Knowledge Discovery and Data Mining, 1-26, Springer (ISBN 1-85233-867-9)
  29. Cios KJ, Kurgan L, 2002. Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms, In: Jain LC, Kacprzyk J, (Ed.), New Learning Paradigms in Soft Computing, 276-322, Springer (ISBN 3-7908-1436-9)

Conference Papers

    2020

  1. Ghadermarzi S, Katuwawala A, Oldfield CJ, Barik A, Kurgan L 2020. Disordered Function Conjunction: On the In-Silico Function Annotation of Intrinsically Disordered Regions, Pacific Symposium on Biocomputing (PSB 2020), 25:171-182, Big Island of Hawaii, Hawaii, U.S.A.
  2. Kurgan L, Radivojac P, Sussman JL, Dunker AK 2020. On the Importance of Computational Biology and Bioinformatics to the Origins and Rapid Progression of the Intrinsically Disordered Proteins Field, Pacific Symposium on Biocomputing (PSB 2020), 25:149-158, Big Island of Hawaii, Hawaii, U.S.A.

    3. 2017

  3. Wu Z, Hu G, Wang K, Kurgan L 2017. Exploratory Analysis of Quality Assessment of Putative Intrinsic Disorder in Proteins, 16th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2017), 722-732, Zakopane, Poland

    4. 2015

  4. YAN J, Kurgan L, 2015. Consensus-based Prediction of RNA and DNA Binding Residues from Protein Sequences, 6th International Conference on Pattern Recognition and Machine Intelligence (PReMI 2015), 501-511, Warsaw, Poland, Springer

    5. 2014

  5. HIJAZI I, Kurgan L, 2014. Improved Prediction of Protein-Small Organic Ligand Binding Sites via Consensus-based Ranking with Linear Regression, Proceedings of the 2014 International Conference on Bioinformatics and Biomedical Technology (ICBBT 2014), 37-42, Gdansk, Poland, IPCBEE

    6. 2012

  6. PENG Z, Kurgan L, 2012. On the Complementarity of the Consensus-based Disorder Prediction, Proceedings of the Pacific Symposium on Biocomputing (PSB 2012), 17:176-187, Big Island of Hawaii, Hawaii, U.S.A., PSB

    7. 2010

  7. CHEN K, MIZIANTY MJ, Kurgan L, 2010. Accurate Prediction of ATP-binding Residues Using Sequence and Sequence derived Structural Descriptors, Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2010), 43-48, Hong Kong, PRC, IEEE Press

    8. 2008

  8. RAK R, Kurgan L, Reformat M, 2008.Use of OWL 2 to Facilitate a Biomedical Knowledge Base Extracted from the GENIA Corpus, Proceedings of the 5th International Workshop on OWL: Experiences and Directions (co-located with the 7th International Semantic Web Conference) (OWLED'08), -, Karlsruhe, Germany
  9. MIZIANTY MJ, Kurgan L, Ogiela M, 2008. Comparative Analysis of the Impact of Discretization on the Classification with Naive Bayes and Semi-Naive Bayes Classifiers, Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA'08), 823-828, San Diego, CA, U.S.A., IEEE Press
  10. STACH W, Kurgan L, Pedrycz W, 2008. Data-Driven Nonlinear Hebbian Learning Method for Fuzzy Cognitive Maps, Proceedings of the IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence) (FUZZ-IEEE'08), 1975-1981, Hong Kong, China, IEEE Press

    11. 2007

  11. Golmohammadi SK, Kurgan L, Crowley B, Reformat M, 2007.Classification of Cell Membrane Proteins, Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies 2007 (FBIT'07), 153-159, Jeju Island, Korea, IEEE Press
  12. CHEN K, Kurgan M, Kurgan L, 2007.Improved Prediction of Relative Solvent Accessibility Using Two-stage Support Vector Regression, Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering (ICBBE'07), 37-40, Wuhan, China, IEEE Press
  13. CHEN K, Kurgan L, Ruan J, 2007.Prediction of Protein Structural Class Using PSI-BLAST Profile Based Collocation of Amino Acid Pairs, Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering (ICBBE'07), 17-20, Wuhan, China, IEEE Press
  14. STACH W, Kurgan L, Pedrycz W, 2007.Parallel Learning of Large Fuzzy Cognitive Maps, Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN'07), 1584-1589, Orlando, FL, U.S.A., IEEE Press
  15. STACH W, Kurgan L, Pedrycz W, 2007.A Framework for a Novel Scalable Fuzzy Cognitive Map Learning Method, Proceedings of the Symposium on Human-Centric Computing and Data Processing (HC2DP'07), 13-14, Banff, Alberta, Canada

    16. 2006

  16. Kurgan L, RAHBARI M, HOMAEIAN L, 2006.Impact of the Predicted Protein Structural Content on Prediction of Structural Classes for the Twilight Zone Proteins, Proceedings of the 5th International Conference on Machine Learning and Applications, Special Session on Applications of Machine Learning in Medicine and Biology(ICMLA'06),180-186, Orlando, FL, U.S.A., IEEE Press
  17. CHEN K, Kurgan L, Ruan J, 2006. Optimization of the Sliding Window Size for Protein Structure Prediction, Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB'06), 366-372, Toronto, Ontario, Canada, IEEE Press
  18. KEDARISETTI K, CHEN K, Kapoor A, Kurgan L, 2006. Prediction of the Number of Helices for the Twilight Zone Proteins, Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB'06), 459-465, Toronto, Ontario, Canada, IEEE Press
  19. STACH W, Kurgan L, Pedrycz W, 2006. Higher-Order Fuzzy Cognitive Maps, Proceedings of the 2006 North American Fuzzy Information Processing Society Conference (NAFIPS'06), 166-171, Montreal, Quebec, Canada, IEEE Press

    20. 2005

  20. RAK R, Kurgan L, Reformat M, 2005. Multi-label Associative Classification of Medical Documents from MEDLINE, Proceedings of the International Conference on Machine Learning and Applications (ICMLA'05), 177-184, Los Angeles, CA, U.S.A., IEEE Press
  21. Kurgan L, HOMAEIAN L, 2005. Prediction of Secondary Protein Structure Content from Primary Sequence Alone - a Feature Selection Based Approach, Proceedings of the International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM'05), 334-345, Leipzig, Germany, Springer Verlag, LNAI 4587
  22. STACH W, Kurgan L, Pedrycz W, Reformat M, 2005. Evolutionary Development of Fuzzy Cognitive Maps, Proceedings of the 2005 IEEE International Conference on Fuzzy Systems (FUZZ IEEE'05), 619-624, Reno, NV, U.S.A., IEEE Press
  23. Kurgan L, KEDARISETTI K, 2005. Secondary Protein Structure Fragments - Feasibility Study in Prediction and Analysis, Proceedings of the Symposium on Human-Centric Computing (HC2'05), 26-36, Banff, Alberta, Canada
  24. STACH W, Kurgan L, Pedrycz W, 2005. Linguistic Signal Prediction with the use of Fuzzy Cognitive Maps, Proceedings of the Symposium on Human-Centric Computing (HC2'05), 64-71, Banff, Alberta, Canada

    25. 2004

  25. Kurgan L, 2004. Reducing Complexity of Rule Based Models via Meta Mining, Proceedings of the 2004 International Conference on Machine Learning and Applications (ICMLA'04), 242-249, Lousville, KY, U.S.A., CSREA Press
  26. Kurgan L, Cios KJ, 2004. Meta Mining Architecture for Supervised Learning, Proceedings of the 7th International Workshop on High Performance and Distributed Mining (HPDM'04),in conjunction with 4th International SIAM Conference on Data Mining 18-26, Lake Buena Vista, FL, U.S.A.
  27. Kurgan L, Dick S, Musilek P, Reformat M, 2004. Shaping the Software Option at the University of Alberta, Proceedings of the 2004 IEEE Canadian Conference on Computer and Software Engineering Education (C3SEE'04), 97-103, Calgary, AB, Canada
  28. STACH W, Kurgan L, Pedrycz W, Reformat M, 2004. Fuzzy Cognitive Maps as a Tool for Modeling Parallel Software Development Projects, Proceedings of the 2004 North American Fuzzy Information Processing Society Conference (NAFIPS'04), 28-33, Banff, Alberta, Canada, IEEE Press
  29. FARHANGFAR A, Kurgan L, Pedrycz W, 2004. Experimental Analysis of Methods for Handling Missing Values in Databases, Proceedings of the Intelligent Computing: Theory and Applications II Conference (ICTA II),in conjunction with SPIE Defense and Security Symposium (formerly AeroSense) 172-182, Orlando, FL, U.S.A., SPIE Press
  30. Li Y., Musilek P, Kurgan L, 2004. Battlecity Revived: Game Design with BDI.net, Proceedings of the 4th ASERC Workshop on Quantitative and Soft Software Engineering (QSSE'04), 24-28, Banff, Alberta, Canada
  31. STACH W, Kurgan L, 2004. Modeling Software Development Projects Using Fuzzy Cognitive Maps, Proceedings of the 4th ASERC Workshop on Quantitative and Soft Software Engineering (QSSE'04), 55-60, Banff, Alberta, Canada

    32. 2003

  32. Kurgan L, Cios KJ, 2003. Fast Class-Attribute Interdependence Maximization (CAIM) Discretization Algorithm, Proceedings of the 2003 International Conference on Machine Learning and Applications (ICMLA'03), 30-36, Los Angeles, CA, U.S.A., CSREA Press

    33. 2002

  33. Kurgan L, Cios KJ, 2002. Ensemble of Classifiers to Improve Accuracy of the CLIP4 Machine Learning Algorithm, Proceedings of the SPIE International Conference on Sensor Fusion: Architectures, Algorithms, and Applications VI (ICSF: AAA VI),in conjunction with SPIE AeroSense Symposium 22-31, Orlando, FL, U.S.A., SPIE Press
  34. Kurgan L, Swiercz W, Cios KJ, 2002. Semantic Mapping of XML Tags Using Inductive Machine Learning, Proceedings of the 2002 International Conference on Machine Learning and Applications (ICMLA'02), 99-109, Las Vegas, NV, U.S.A., CSREA Press
  35. Kurgan L, Cios KJ, Trombley M, 2002. The WWW Based Data Mining Toolbox Architecture, Proceedings of the 6th International Conference on Neural Networks and Soft Computing (ICNNSN'02), 855-860, Zakopane, Poland, Springer Verlag

    36. 2001

  36. Kurgan L, Cios KJ, 2001. Discretization Algorithm that Uses Class-Attribute Interdependence Maximization, Proceedings of the 2001 International Conference on Artificial Intelligence (IC-AI'01), 980-987, Las Vegas, NV, U.S.A.

Abstracts and Presentations

  1. Kurgan L, flDPnn and DisoRDPbind Predictors of Disorder and Disorder Binding, Invited presentation, CAID Meeting, ELIXIR Community Meetings, February 2021
  2. Kurgan L, Computational Prediction of Intrinsic Disorder: From Humble Beginnings to Modern Resources, Invited highlight presentation, 2020 Pacific Symposium on Biocomputing (PSB 2020), Big Island of Hawaii, Hawaii, U.S.A., January 2020
  3. Ghadermarzi S, Katuwawala A, Oldfield CJ, Barik A, Kurgan L, In-Silico Function Annotation of Intrinsically Disordered Regions, Pacific Symposium on Biocomputing (PSB 2020) Big Island of Hawaii, Hawaii, U.S.A., January 2020
  4. Kurgan L, Quality Assessment for Intrinsic Disorder Predictions Bioinformatics in Torun (BIT’19), Torun, Poland, June 2019
  5. Kurgan L, Computational Methods for Targets Selection and Characterization for Structural Genomics, Keynote presentation , 2018 International Conference on Bioinformatics (INCOB 2018), New Delhi, India, September 2018
  6. Kurgan L, Making Intrinsic Disorder Prediction Practical with Quality Assessment, 2018 Cold Spring Harbor Asia Conference on Frontiers in Computational Biology & Bioinformatics, Suzhou, China, September 2018
  7. Kurgan L, Selection and Characterization of Targets for Structural Genomics, Center for Bioinformatics Research, Indiana University, Bloomington, IN, U.S.A., September 2017
  8. Kurgan L, Selection and Characterization of Targets for Structural Genomics, Biomedical Informatics Hutton Lecture Series, Cincinnati Children's Hospital, Cincinnati, U.S.A., September 2017
  9. Murray G, Meng F, Kurgan L, Donahue H, Osteocytic Proteins Upregulated by Mechanical Signals Display Increased Functional Specificity, Journal of Bone And Mineral Research 32, S350-S351, 2017
  10. Kurgan L, Target Selection for Structural Genomics, Virginia Commonwealth University, Department of Biostatistics, Richmond, VA, U.S.A., April 2017
  11. Kurgan L, Structural Coverage of Protein Universe, Temple University, Department of Computer and Information Sciences, Philadelphia, PA, U.S.A., September 2016
  12. Kurgan L, Structural Coverage of Protein Universe, Tianjin University, Tianjin, China, June 2016
  13. Kurgan L, Structural Coverage of Protein Universe, Chern Institute of Mathematics at the Nankai University, Tianjin, China, June 2016
  14. Kurgan L, High-throughput Prediction of Disordered Flexible Linker Regions, Protein and RNA Structure Prediction Conference, Punta Cana, Dominican Republic, December 2015
  15. Kurgan L, Structural Genomics and Protein Universe, Virginia Commonwealth University, Department of Computer Science, Richmond, VA, U.S.A., September 2015
  16. Kurgan L, Propensity for X-ray Crystallography and Structural Coverage of the Protein Universe, University of Saskatchewan, Saskatoon, Canada, November 2014
  17. PENG Z, Kurgan L, High-throughput Prediction of Protein-protein, -RNA and -DNA Interactions in Disordered Regions, Gordon Research Conference, Intrinsically Disordered Proteins, Easton, MA, U.S.A., July 2014
  18. Kurgan L, Accurate Human Structural Proteome-wide Characterization of Protein-drug Interactions, Nankai University, Mathematics and Informatics for Public Health Conference, Tianjin, China, May 2014
  19. Kurgan L, Characterization of Propensity for X-ray Crystallography of Protein Chains, Department of Medical Genetics Seminar Series, University of Alberta, Edmonton, AB, Canada, March 2014
  20. PENG Z, Kurgan L, High-throughput Prediction of Protein-protein, Protein-RNA and Protein-DNA Interactions Mediated by Disordered Regions, Protein and RNA Structure Prediction Conference, Riviera Maya, Mexico, December 2013
  21. MIZIANTY MJ, FAN X, YAN J, Chalmers E, Woloschuk C, Joachimiak A, Kurgan L, Structural Coverage using X-ray Crystallography for a Current Snapshot of the Protein Universe, 3DSig, Berlin, Germany, July 2013
  22. Kurgan L, Characterization of Propensity for X-ray Crystallography of Protein Chains, University of South Florida, Tampa, USA, June 2013
  23. Kurgan L, Inverse Ligand Binding Prediction Provides Insights into Toxicity Induced by Cyclosporine A, Chern Institute of Mathematics at the Nankai University, Tianjin, China, June 2013
  24. Kurgan L, Computational Prediction of microRNA Targets in Animals, Chern Institute of Mathematics at the Nankai University, Tianjin, China, June 2013
  25. Kurgan L, Disorder in Proteins: Abundance and Functional Characterization in 1000 Proteomes across the Three Domains of Life and Viruses, Bioinformatics in Torun (BIT 2012) Conference, Torun, Poland, September 2012
  26. Kurgan L, Disorder in Proteins: Functional Lack of Structure, Chern Institute of Mathematics at the Nankai University, Tianjin, China, June 2012
  27. Kurgan L, Disorder in Proteins: Current Characterization Efforts, Great Lakes Bioinformatics Conference (GLBIO), Ann Arbor, MI, U.S.A., May 2012
  28. PENG Z, MIZIANTY MJ, Kurgan L, New Insights into Computational Disorder Prediction, Protein and RNA Structure Prediction Conference, Riviera Maya, Mexico, December 2011
  29. CHEN K, Kurgan L, Structure-based Detection of Distant Functional Relations, Multi-Pole Approach to Structural Biology, Warsaw, Poland, November 2011
  30. MIZIANTY MJ, Bhargava A, de Brum-Fernandes AJ, Dixon J, Durand M, Harrison R, Komarova S, Kurgan L, Lucena-Fernandes MF, Monolson MF, Sims S, Multivariate Diagnostic Model for Osteoarthritis Highlights the Importance of Osteoclastogenesis and Expression of Interleukin 1 Receptors, Canadian Arthritis Network, 2011 CAN Annual Scientific Conference, Quebec City, QC, Canada, October 2011
  31. Maria SM, Bhargava A, de Brum-Fernandes AJ, Dixon J, Durand M, Harrison R, Komarova S, Kurgan L, Lucena-Fernandes MF, Monolson MF, MIZIANTY MJ, Sims S, Mineral Homeostasis is Altered in Rheumatoid Arthritis Patients, Canadian Arthritis Network, 2011 CAN Annual Scientific Conference, Quebec City, QC, Canada, October 2011
  32. Groenendyk J, MIZIANTY MJ, Kurgan L, Michalak M, A Genome-wide siRNA Screen Identification of Proteins Involved in the Modulation of ER Stress, 9th International Calreticulin Workshop, Copenhagen, Denmark, August 2011
  33. Kurgan L, Inverse Docking as a Platform to Elucidate Mechanisms Defining Adverse Reactions to Drugs, 2011 China-Canada Annual Workshop on Mathematical Modeling of Infectious Diseases, Beijing, China, August 2011
  34. MIZIANTY MJ, STACH W, CHEN K, KEDARISETTI KD, MIRI DISFANI F, Kurgan L, Improved Sequence-based Prediction of Disordered Regions with Multilayer Fusion of Multiple Information Sources, ECCB, 9th European Conference on Computational Biology (ECCB), Ghent, Belgium, September 2010
  35. Kurgan L, In-silico Characterization of Crystallization Propensity of Protein Chains, Department of Computer Science at the Purdue University, West Lafayette, IN, U.S.A., June 2010
  36. Kurgan L, Characterization of Crystallization Propensity of Protein Chains , Center for Computational Biology and Bioinformatics, IUPUI, Indianapolis, IN, U.S.A., April 2010
  37. Kurgan L, Characterization of Crystallization Propensity of Protein Chains for Structural Genomics, Department of Automation at the Shanghai Jiao Tong University, Shanghai, China, January 2010
  38. Kurgan L, Characterization of Crystallization Propensity of Protein Chains for Structural Genomics, Chern Institute of Mathematics at the Nankai University, Tianjin, China, January 2010
  39. de Brum-Fernandes AJ, Boire G, Dixon J, Durand M, Harrison R, Komarova S, Kurgan L, Monolson MF, Maria O, MIZIANTY MJ, Nabavi N, RAK R, Roy M, Sims S, Trebec-Reynolds DP, The in vitro osteoclast differentiation in arthritis (IODA) project: Osteoclastogenesis as a marker of presence and activity of disease in arthritis, Canadian Rheumatology Association, 2010 Canadian Rheumatology Association Annual Meeting, Quebec City, QC, Canada, February 2010
  40. de Brum-Fernandes AJ, Boire G, Dixon J, Durand M, Harrison R, Komarova S, Kurgan L, Monolson MF, Maria O, MIZIANTY MJ, Nabavi N, RAK R, Roy M, Sims S, Trebec-Reynolds DP, The In vitro Osteoclast Differentiation in Arthritis (IODA) project: Osteoclastogenesis as a Marker of Presence and Activity of Disease in Arthritis, Canadian Arthritis Network, 2009 CAN Annual Scientific Conference, Vancouver, BC, Canada, November 2009
  41. Kurgan L, Crystallization propensity of protein chains, Keynote presentation , 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2009), Beijing, China, June 2009
  42. KEDARISETTI K, MIZIANTY MJ, Dick S, Kurgan L, ß-strand segments prediction based on protein sequence and predicted neighboring structural information, ISCB (International Society for Computational Biology), 17th Annual International Conference on Intelligent Systems for Molecular Biology, Stockholm, Sweden, June-July 2009
  43. Kurgan L, Tuszynski J, Inverse docking as a platform to elucidate mechanisms defining adverse reactions to drugs, Nankai University and MITACS, 2009 China-Canada Biomedical Problem Solving Workshop , Tianjin, China, May 2009
  44. Dufort P, Durand M, Allard-Chamard H, Gallant M, Komarova S, Monolson MF, Harrison R, Dixon J, Sims S, Kurgan L, Boire G, de Brum-Fernandes AJ, Increased Osteoclastogenesis in Patients with Rheumatoid Arthritis, Americal College of Rheumatology, Arthritis and Rheumatism, 58(9):S768-S768, (ACR'2008) San Francisco, CA, U.S.A., October 2008
  45. de Brum-Fernandes AJ, Boire G, Dixon J, Durand M, Harrison R, Komarova S, Kurgan L, Monolson MF, Maria O, RAK R, Roy M, Sims S, Osteoclastogenesis as a Marker of Presence and Activity of Disease in Rheumatoid Arthritis and Osteoarthritis, Canadian Arthritis Network, 2008 CAN Annual Scientific Conference, Toronto, ON, Canada, October 2008
  46. CHEN K, Kurgan L, Investigation of Atomic Level Patterns in Protein-ligand Interactions, ISCB (International Society for Computational Biology), 16th Annual International Conference on Intelligent Systems for Molecular Biology, Toronto, ON, Canada, July 2008
  47. KEDARISETTI K, Dick S, Kurgan L, Ensembles of Secondary Structure Predictors for Sequence-based ß-residue Pair Prediction, ISCB (International Society for Computational Biology), 16th Annual International Conference on Intelligent Systems for Molecular Biology, Toronto, ON, Canada, July 2008
  48. de Brum-Fernandes AJ, Dufort P, Boire G, Dixon J, Durand M, Harrison R, Komarova S, Kurgan L, Monolson MF, RAK R, Sims S, Osteoclast-related Biomarkers in Rheumatoid Arthritis, CCTC, 14th Canadian Connective Tissue Conference, Montreal, Quebec, Canada, June 2008
  49. RAHBARI M, Truhachev D, Kurgan L, Protein Content Prediction Based on Principal Component Analysis and Support Vector Machine Regression, MITACS, Second Congress Canada-France 2008, Montreal, Quebec, Canada, June 2008
  50. Maria O, Durand M, de Brum-Fernandes AJ, Boire G, Monolson MF, Dixon J, Sims S, Harrison R, Kurgan L, Komarova S, Investigations of Gene Expression in Osteoclasts Formed from Peripheral Blood Monocytes of Patients with Rheumatoid Arthritis, Canadian Arthritis Network, 2007 CAN Annual Scientific Conference, Halifax, NS, Canada, October 2007
  51. RAK R, Boire G, de Brum-Fernandes AJ, Dixon J, Harrison R, Komarova S, Monolson MF, Sims S, Kurgan L, Using Data Mining to Find Markers Related to the Role of Osteoclasts in Bone and Joint Destruction, Canadian Arthritis Network, 2007 CAN Annual Scientific Conference, Halifax, NS, Canada, October 2007
  52. KEDARISETTI K, Dick S, Kurgan L, Searching for Factors Involved in Misfolding of the PrPC via In-silico Techniques, Alberta Prion Research Institute and PrioNet Canada, PrPCanada 2007, Calgary, AB, Canada, February 2007
  53. Kurgan L, Discovering Structure in Data - Fast Classification Using the DataSqueezer Algorithm, Chern Institute of Mathematics at the Nankai University, Tianjin, China, June 2006
  54. Kurgan L, What, Why and How of Computational Protein Structure Prediction, Center for Mathematical Biology at the University of Alberta, PIMS-MITACS Mathematical Biology Seminar Series, Edmonton, AB, Canada, March 2006
  55. Kurgan L, Discovering Structure in the Data, MITACS-MSRI-CMM sponsored meeting at the Banff International Research Station , Workshop on Growth and Control of Tumours, Banff, AB, Canada, October 2005
  56. FARHANGFAR A, Kurgan L, Pedrycz W, Novel Method for Handling Missing Values in Databases Based on Mean Pre-Imputation, Confidence Intervals and Boosting, MITACS, MITACS 5th Annual Conference, Halifax, Nova Scotia, Canada, June 2004
  57. Kurgan L, The Ensemble of Classifiers to Improve Accuracy of SPECT Heart Image Analysis System, University of Colorado Center for Computational Biology, 2002 Annual Meeting Poster Session, Denver, CO, U.S.A., March 2002
  58. Kurgan L, Data Mining and Knowledge Discovery, University of Colorado at Denver, Department of Computer Science and Engineering, the 2002 Computer Science Seminars, Denver, CO, U.S.A., March 2002
  59. Cios KJ, Kurgan L, Mitchell S, Bailey M, Duckett D, Gau K, Report for the OAI Phase I Collaborative Core Research Project on Data Mining and Knowledge Discovery, Ohio Aerospace Institute (OAI), 2000 OAI Collaborations Forum, Cleveland, OH, U.S.A., 2000
  60. Kurgan L, CLIP4 Inductive Machine Learning Algorithm, University of Toledo, 21 Annual Sigma Xi Graduate Research Symposium, Toledo, OH, U.S.A., May 2000

Theses

  1. KATUWAWALA A, 2021. Computational Analysis and Prediction of Intrinsic Disorder and Intrinsic Disorder Functions in Proteins, Ph.D. thesis, Virginia Commonwealth University, department of Computer Science, Richmond, Virginia, USA
  2. WANG C, 2018. High-throughput Prediction and Analysis of Drug-protein Interactions in the Druggable Human Proteome, Ph.D. thesis, Virginia Commonwealth University, department of Computer Science, Richmond, Virginia, USA
  3. MENG F, 2018. Fast and Accurate Computational Prediction of Functions of Intrinsic Disorder in Proteins, Ph.D. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  4. YAN J, 2016. Prediction and Characterization of DNA and RNA Binding Residues from Protein Sequence: State-of-the-art, Novel Predictors and Proteome-scale Analysis, Ph.D. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  5. FAN X, 2015. High-throughput Computational Characterization and Prediction of MicroRNA Targets, Ph.D. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  6. PENG Z, 2014. Large-scale Characterization of Intrinsic Disorder and High-throughput Prediction of RNA, DNA, and Protein Binding Mediated by Intrinsic Disorder, Ph.D. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  7. MIZIANTY MJ, 2013. Computational Support Systems for Prediction and Characterization of Protein Crystallization Outcomes , Ph.D. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  8. KEDARISETTI K, 2012. Computational Prediction of Strand Residues from Protein Sequences, Ph.D. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  9. MIRI DISFANI F, 2012. Sequence-based prediction and characterization of disorder-to-order transitioning binding sites in proteins, M.Sc. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  10. CHEN K, 2011. In-silico Characterization and Prediction of Protein-Small Ligand Interactions, Ph.D. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  11. STACH W, 2010. Learning and Aggregation of Fuzzy Cognitive Maps - an Evolutionary Approach, Ph.D. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  12. RAK R, 2009. Associative Classification, Linguistic Entity Relationship Extraction, and Description-Logic Representation of Biomedical Knowledge Applied to MEDLINE, Ph.D. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  13. RAHBARI M, 2008. Hybrid Models for Protein Secondary Structure Content Prediction, M.Sc. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  14. HOMAEIAN L, 2006. Towards Improving Accuracy of Protein Content Prediction for Low Homology Sequences, M.Sc. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  15. KEDARISETTI K, 2005. Computational Prediction of Three State Secondary Structure for Protein Structural Fragments, M.Sc. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  16. FARHANGHFAR A, 2005. New Framework for Imputation of Missing Values, M.Sc. thesis, University of Alberta, department of Electrical and Computer Engineering, Edmonton, Alberta, Canada
  17. Kurgan L, 2003. Meta Mining System for Supervised Learning,honor Ph.D. thesis, University of Colorado at Boulder, department of Computer Science, Boulder, CO, U.S.A.
  18. Kurgan L, 1999. Using Methods of Computerized Image Analysis and Processing for Diagnosis of SPECT Images to Help Diagnosing Coronary Artery Diseases,honor M.Sc. thesis, AGH University of Science and Technology, department of Automation and Robotics, Krakow, Poland
The dissertations are copyrighted. You may download an electronic copy and make multiple hard copies, but they are restricted to scholarly use only. No other usage of these dissertations, in whole or in part, is permitted without prior written permission of the corresponding authors. These materials are presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.