World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
73
Citations
28773
World Ranking
1555
National Ranking
810

Biology and Biochemistry

D-Index
71
Citations
25494
World Ranking
6526
National Ranking
3037

Research.com Recognitions

  • 2018 - Fellow of the Indian National Academy of Engineering (INAE)
  • 2012 - ACM Senior Member

Overview

Lukasz Kurgan is affiliated with Virginia Commonwealth University in the United States. Their primary field of research is in Biochemistry, Genetics, and Molecular Biology, with a total of 189 publications. Subfields of study include Molecular Biology, Materials Chemistry, Computational Theory and Mathematics, Spectroscopy, and Biomedical Engineering.

The scientist's research topics encompass areas such as Protein Structure and Dynamics, Machine Learning in Bioinformatics, RNA and protein synthesis mechanisms, Enzyme Structure and Function, Genomics and Phylogenetic Studies, Genetics, Bioinformatics, and Biomedical Research, and Bioinformatics and Genomic Networks.

Some of the recent publications by Lukasz Kurgan include:

  • Critical assessment of protein intrinsic disorder prediction, 2021, Nature Methods
  • flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions, 2021, Nature Communications
  • iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization, 2021, Nucleic Acids Research
  • Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease, 2020, Medical Image Analysis
  • DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning, 2021, Briefings in Bioinformatics

Frequent co-authors of Lukasz Kurgan include:

  • Bi Zhao
  • Sushmita Basu
  • Gang Hu
  • Jiangning Song
  • Zhonghua Wu

The scientist regularly publishes in several venues such as Faculty Opinions - Post-Publication Peer Review of the Biomedical Literature, Methods in Molecular Biology, Computational and Structural Biotechnology Journal, Nucleic Acids Research, and Briefings in Bioinformatics.

Lukasz Kurgan has contributed to book publications, including a titled work published by World Scientific: Machine Learning in Bioinformatics of Protein Sequences (2022).

Awards conferred to the scientist include the Fellow of the Indian National Academy of Engineering (INAE) in 2018 and the ACM Senior Member designation in 2012.

Best Publications

  • Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

    Daniel J. Klionsky;Amal Kamal Abdel-Aziz;Sara Abdelfatah;Mahmoud Abdellatif

  • Data Mining: A Knowledge Discovery Approach

    Krzysztof J. Cios;Witold Pedrycz;Roman W. Swiniarski;Lukasz Andrzej Kurgan

  • D2P2: database of disordered protein predictions

    Matt E. Oates;Pedro Romero;Takashi Ishida;Mohamed F. Ghalwash

  • Genetic learning of fuzzy cognitive maps

    Wojciech Stach;Lukasz Kurgan;Witold Pedrycz;Marek Reformat

  • CAIM discretization algorithm

    L.A. Kurgan;K.J. Cios

  • A survey of Knowledge Discovery and Data Mining process models

    Lukasz A. Kurgan;Petr Musilek

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

    Alireza Farhangfar;Lukasz Kurgan;Jennifer Dy

  • Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life

    Zhenling Peng;Jing Yan;Xiao Fan;Marcin J. Mizianty

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

    Fatemeh Miri Disfani;Wei-Lun Hsu;Marcin J. Mizianty;Christopher J. Oldfield

  • Knowledge discovery approach to automated cardiac SPECT diagnosis

    Lukasz A. Kurgan;Krzysztof J. Cios;Ryszard Tadeusiewicz;Marek Ogiela

  • A Novel Framework for Imputation of Missing Values in Databases

    A. Farhangfar;L.A. Kurgan;W. Pedrycz

  • flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions.

    Gang Hu;Akila Katuwawala;Kui Wang;Zhonghua Wu

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

    Eshel Faraggi;Tuo Zhang;Tuo Zhang;Yuedong Yang;Yuedong Yang;Lukasz A. Kurgan;Lukasz A. Kurgan

  • iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization.

    Zhen Chen;Pei Zhao;Chen Li;Fuyi Li;Fuyi Li

  • Trends in Data Mining and Knowledge Discovery

    Krzysztof J. Cios;Krzysztof J. Cios;Lukasz A. Kurgan

  • DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues.

    Jing Yan;Lukasz Kurgan

  • Structural disorder in viral proteins.

    Bin Xue;David Blocquel;David Blocquel;Johnny Habchi;Johnny Habchi;Alexey V Uversky

  • Improved sequence-based prediction of disordered regions with multilayer fusion of multiple information sources

    Marcin J. Mizianty;Wojciech Stach;Ke Chen;Kanaka Durga Kedarisetti

  • Classifier ensembles for protein structural class prediction with varying homology.

    Kanaka Durga Kedarisetti;Lukasz Kurgan;Scott Dick

  • Prediction of protein structural class using novel evolutionary collocation-based sequence representation.

    Ke Chen;Lukasz A. Kurgan;Jishou Ruan

  • 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

Frequent Co-Authors

Vladimir N. Uversky
Vladimir N. Uversky University of South Florida
Krzysztof J. Cios
Krzysztof J. Cios Virginia Commonwealth University
Witold Pedrycz
Witold Pedrycz University of Alberta
Hua Zhang
Hua Zhang City University of Hong Kong
A. Keith Dunker
A. Keith Dunker Indiana University
Yaoqi Zhou
Yaoqi Zhou Griffith University
Marek Michalak
Marek Michalak University of Alberta
Jiangning Song
Jiangning Song Monash University
Claudio Hetz
Claudio Hetz University of Chile
Christopher J. Oldfield
Christopher J. Oldfield Virginia Commonwealth University

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