Lukasz A. Kurgan

发表

Sequence based residue depth prediction using evolutionary information and predicted secondary structure

Lukasz A. Kurgan, Tuo Zhang, Ke Chen, 2008, BMC Bioinformatics.

Evolutionary Development of Fuzzy Cognitive Maps

Witold Pedrycz, Lukasz A. Kurgan, Marek Reformat, 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

Corrigendum to: Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains

Lukasz A. Kurgan, Jian Zhang, Zhiqiang Ma, 2020, Briefings Bioinform..

Prediction of integral membrane protein type by collocated hydrophobic amino acid pairs

Lukasz A. Kurgan, Li Du, Ke Chen, 2009, J. Comput. Chem..

Review and comparative assessment of sequence‐based predictors of protein‐binding residues

Lukasz A. Kurgan, Jian Zhang, Lukasz Kurgan, 2018, Briefings Bioinform..

Parallel Learning of Large Fuzzy Cognitive Maps

Witold Pedrycz, Lukasz A. Kurgan, Wojciech Stach, 2007, 2007 International Joint Conference on Neural Networks.

MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins

Lukasz A. Kurgan, Bin Xue, A. Keith Dunker, 2012, Bioinform..

Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

Lukasz A. Kurgan, Ce Zheng, Lukasz Kurgan, 2008, BMC Bioinformatics.

Highly scalable and robust rule learner: performance evaluation and comparison

Lukasz A. Kurgan, Scott Dick, Krzysztof J. Cios, 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization

Chen Wang, Lukasz A. Kurgan, Fanchi Meng, 2017, BMC Bioinformatics.

Prediction of Secondary Protein Structure Content from Primary Sequence Alone - A Feature Selection Based Approach

Lukasz A. Kurgan, Leila Homaeian, Lukasz Kurgan, 2005, MLDM.

Fast Class-Attribute Interdependence Maximization (CAIM) Discretization Algorithm

Lukasz A. Kurgan, Krzysztof J. Cios, 2003, ICMLA.

Quality assessment for the putative intrinsic disorder in proteins

Chen Wang, Lukasz A. Kurgan, Gang Hu, 2018, Bioinform..

D2P2: database of disordered protein predictions

Lukasz A. Kurgan, Mohamed F. Ghalwash, Zoran Obradovic, 2012, Nucleic Acids Res..

Learning of Fuzzy Cognitive Maps Using Density Estimate

Witold Pedrycz, Lukasz A. Kurgan, Wojciech Stach, 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

mi-DS: Multiple-Instance Learning Algorithm

Lukasz A. Kurgan, Krzysztof J. Cios, Rosalyn Hobson Hargraves, 2013, IEEE Transactions on Cybernetics.

Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology

Lukasz A. Kurgan, Scott D. Bass, 2009, Scientometrics.

Prediction of structural classes for protein sequences and domains - Impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy

Lukasz A. Kurgan, Leila Homaeian, Lukasz Kurgan, 2006, Pattern Recognit..

Knowledge discovery approach to automated cardiac SPECT diagnosis

Lukasz A. Kurgan, Marek R. Ogiela, Krzysztof J. Cios, 2001, Artif. Intell. Medicine.

SPINE X: Improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles

Lukasz A. Kurgan, Yaoqi Zhou, Yuedong Yang, 2012, J. Comput. Chem..

Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity

Huilin Wang, Liubin Feng, Geoffrey I. Webb, 2017, Briefings Bioinform..

A Novel Framework for Imputation of Missing Values in Databases

Witold Pedrycz, Lukasz A. Kurgan, Alireza Farhangfar, 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

Data-driven Nonlinear Hebbian Learning method for Fuzzy Cognitive Maps

Witold Pedrycz, Lukasz A. Kurgan, Wojciech Stach, 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

Reducing complexity of rule based models via meta mining

Lukasz A. Kurgan, 2004, 2004 International Conference on Machine Learning and Applications, 2004. Proceedings..

Sequence-based Gaussian network model for protein dynamics

Lukasz A. Kurgan, Hua Zhang, Hua Zhang, 2013, Bioinform..

Improved Sequence-Based Prediction of Strand residues

Lukasz A. Kurgan, Scott Dick, Marcin J. Mizianty, 2011, J. Bioinform. Comput. Biol..

A new synaptic plasticity rule for networks of spiking neurons

Lukasz A. Kurgan, Krzysztof J. Cios, Waldemar Swiercz, 2006, IEEE Transactions on Neural Networks.

Discretization as the enabling technique for the Naïve Bayes and semi-Naïve Bayes-based classification

Lukasz A. Kurgan, Marek R. Ogiela, Marcin J. Mizianty, 2010, Knowl. Eng. Rev..

Multi-label associative classification of medical documents from MEDLINE

Lukasz A. Kurgan, Marek Reformat, Rafal Rak, 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).

In-silico prediction of disorder content using hybrid sequence representation

Lukasz A. Kurgan, Yaoqi Zhou, Tuo Zhang, 2011, BMC Bioinformatics.

Numerical and Linguistic Prediction of Time Series With the Use of Fuzzy Cognitive Maps

Witold Pedrycz, Lukasz A. Kurgan, Wojciech Stach, 2008, IEEE Transactions on Fuzzy Systems.

Impact of imputation of missing values on classification error for discrete data

Lukasz A. Kurgan, Jennifer G. Dy, Alireza Farhangfar, 2008, Pattern Recognit..

Prediction of protein structural class using novel evolutionary collocation‐based sequence representation

Lukasz A. Kurgan, Ke Chen, Jishou Ruan, 2008, J. Comput. Chem..

On the Complementarity of the Consensus-Based Disorder Prediction

Lukasz A. Kurgan, Zhen-Ling Peng, Lukasz Kurgan, 2011, Pacific Symposium on Biocomputing.

Highly accurate and consistent method for prediction of helix and strand content from primary protein sequences

Lukasz A. Kurgan, Kui Wang, Krzysztof J. Cios, 2005, Artif. Intell. Medicine.

A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation

Lukasz A. Kurgan, Marek Reformat, Rafal Rak, 2008, Data Knowl. Eng..

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

Lukasz A. Kurgan, Krzysztof J. Cios, Ke Chen, 2008, BMC Bioinformatics.

PFRES: protein fold classification by using evolutionary information and predicted secondary structure

Lukasz A. Kurgan, Ke Chen, Ke Chen, 2007, Bioinform..

Classification of Cell Membrane Proteins

Lukasz A. Kurgan, Marek Reformat, Seyed Koosha Golmohammadi, 2007, 2007 Frontiers in the Convergence of Bioscience and Information Technologies.

PDID: database of molecular-level putative protein-drug interactions in the structural human proteome

Chen Wang, Lukasz A. Kurgan, Lei Xie, 2016, Bioinform..

A comment on "Prediction of protein structural classes by a new measure of information discrepancy"

Lukasz A. Kurgan, Scott Dick, Kanaka Durga Kedarisetti, 2006, Comput. Biol. Chem..

Genetic learning of fuzzy cognitive maps

Witold Pedrycz, Lukasz A. Kurgan, Marek Reformat, 2005, Fuzzy Sets Syst..

Semantic Mapping of XML Tags Using Inductive Machine Learning

Lukasz A. Kurgan, Krzysztof J. Cios, Waldemar Swiercz, 2002, ICMLA.

Optimization of the Sliding Window Size for Protein Structure Prediction

Lukasz A. Kurgan, Ke Chen, Jishou Ruan, 2006, 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology.

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

Lukasz A. Kurgan, Marcin J. Mizianty, Lukasz Kurgan, 2009, BMC Bioinformatics.

Recognition of Partially Occluded and Rotated Images With a Network of Spiking Neurons

Lukasz A. Kurgan, Javier Montero, Krzysztof J. Cios, 2010, IEEE Transactions on Neural Networks.

CLIP4: Hybrid inductive machine learning algorithm that generates inequality rules

Lukasz A. Kurgan, Krzysztof J. Cios, K. Cios, 2004, Inf. Sci..

Neural Networks in Bioinformatics

Lukasz A. Kurgan, Ke Chen, Ke Chen, 2012, Handbook of Natural Computing.

Impact of the Predicted Protein Structural Content on Prediction of Structural Classes for the Twilight Zone Proteins

Lukasz A. Kurgan, Leila Homaeian, Mandana Rahbari, 2006, 2006 5th International Conference on Machine Learning and Applications (ICMLA'06).

Comprehensive overview and assessment of computational prediction of microRNA targets in animals

Lukasz A. Kurgan, Xiao Fan, Lukasz Kurgan, 2015, Briefings Bioinform..

Exploratory Analysis of Quality Assessment of Putative Intrinsic Disorder in Proteins

Lukasz A. Kurgan, Kui Wang, Gang Hu, 2017, ICAISC.

Improved machine learning method for analysis of gas phase chemistry of peptides

Lukasz A. Kurgan, Krzysztof J. Cios, Karen Kafadar, 2008, BMC Bioinformatics.

Sequence-based prediction of protein crystallization, purification and production propensity

Lukasz A. Kurgan, Marcin J. Mizianty, Lukasz Kurgan, 2011, Bioinform..

CAIM discretization algorithm

Lukasz A. Kurgan, Krzysztof J. Cios, K. Cios, 2004, IEEE Transactions on Knowledge and Data Engineering.

Machine Learning Algorithms Inspired by the Work of Ryszard Spencer Michalski

Lukasz A. Kurgan, Krzysztof J. Cios, K. Cios, 2010, Advances in Machine Learning I.

18 Neural Networks in

Lukasz A. Kurgan, Bioinformatics Chen, Lukasz Kurgan, 2012 .

Use of OWL 2 to Facilitate a Biomedical Knowledge Base Extracted from the GENIA Corpus

Lukasz A. Kurgan, Marek Reformat, Rafal Rak, 2008, OWLED.

Secondary structure-based assignment of the protein structural classes

Lukasz A. Kurgan, Hua Zhang, Shiyi Shen, 2008, Amino Acids.

Prediction of the Number of Helices for the Twilight Zone Proteins

Lukasz A. Kurgan, Ke Chen, Kanaka Durga Kedarisetti, 2006, 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology.

Machine learning in the life sciences

Lukasz A. Kurgan, Marek Reformat, Krzysztof J. Cios, 2007, IEEE Engineering in Medicine and Biology Magazine.

DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences

Lukasz A. Kurgan, Fanchi Meng, Lukasz Kurgan, 2016, Bioinform..

Accurate sequence-based prediction of catalytic residues

Lukasz A. Kurgan, Tuo Zhang, Ke Chen, 2008, Bioinform..

HuMiTar: A sequence-based method for prediction of human microRNA targets

Lukasz A. Kurgan, Ke Chen, Jishou Ruan, 2008, Algorithms for Molecular Biology.

Accurate prediction of ATP-binding residues using sequence and sequence-derived structural descriptors

Lukasz A. Kurgan, Ke Chen, Marcin J. Mizianty, 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

A divide and conquer method for learning large Fuzzy Cognitive Maps

Witold Pedrycz, Lukasz A. Kurgan, Wojciech Stach, 2010, Fuzzy Sets Syst..