Lukasz Kurgan

20.2k total citations · 1 hit paper
255 papers, 12.2k citations indexed

About

Lukasz Kurgan is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Lukasz Kurgan has authored 255 papers receiving a total of 12.2k indexed citations (citations by other indexed papers that have themselves been cited), including 211 papers in Molecular Biology, 63 papers in Materials Chemistry and 36 papers in Computational Theory and Mathematics. Recurrent topics in Lukasz Kurgan's work include Protein Structure and Dynamics (139 papers), Machine Learning in Bioinformatics (104 papers) and RNA and protein synthesis mechanisms (84 papers). Lukasz Kurgan is often cited by papers focused on Protein Structure and Dynamics (139 papers), Machine Learning in Bioinformatics (104 papers) and RNA and protein synthesis mechanisms (84 papers). Lukasz Kurgan collaborates with scholars based in Canada, United States and China. Lukasz Kurgan's co-authors include Vladimir N. Uversky, Marcin J. Mizianty, Ke Chen, Witold Pedrycz, Zhenling Peng, Krzysztof J. Cios, Wojciech Stach, Bin Xue, Jing Yan and Jishou Ruan and has published in prestigious journals such as Chemical Reviews, Nucleic Acids Research and Nature Communications.

In The Last Decade

Lukasz Kurgan

246 papers receiving 11.9k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

D2P2: database of disordered protein predictions

2012 524 citations Marcin J. Mizianty, Zsuzsanna Dosztányi et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Lukasz Kurgan Canada	63	8.6k	1.9k	1.9k	1.4k	568	255	12.2k
Jianxin Wang China	69	10.3k 1.2×	2.1k 1.1×	1.8k 0.9×	4.0k 2.9×	917 1.6×	1.1k	22.1k
Alfonso Valencia Spain	82	17.7k 2.1×	2.2k 1.1×	2.3k 1.2×	1.7k 1.2×	327 0.6×	384	22.7k
Temple F. Smith United States	50	12.5k 1.4×	2.5k 1.3×	870 0.5×	582 0.4×	444 0.8×	152	16.6k
Quan Zou China	76	15.1k 1.7×	2.2k 1.1×	724 0.4×	2.4k 1.7×	379 0.7×	554	20.1k
Dong Xu United States	72	9.7k 1.1×	821 0.4×	1.3k 0.7×	858 0.6×	396 0.7×	454	16.2k
Michael Schroeder Germany	45	5.2k 0.6×	2.8k 1.5×	612 0.3×	2.1k 1.5×	1.9k 3.3×	224	12.8k
Zoran Obradović United States	51	14.4k 1.7×	1.2k 0.6×	4.5k 2.3×	566 0.4×	318 0.6×	319	19.5k
Eytan Ruppin Israel	78	12.4k 1.4×	2.0k 1.0×	281 0.1×	1.7k 1.3×	231 0.4×	339	19.9k
Jinyan Li China	46	3.5k 0.4×	1.8k 0.9×	1.3k 0.7×	1.3k 0.9×	1.3k 2.2×	488	9.5k
William E. Hart United States	30	6.0k 0.7×	873 0.4×	934 0.5×	3.0k 2.1×	72 0.1×	139	14.3k

Countries citing papers authored by Lukasz Kurgan

Since Specialization

Citations

This map shows the geographic impact of Lukasz Kurgan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lukasz Kurgan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lukasz Kurgan more than expected).

Fields of papers citing papers by Lukasz Kurgan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lukasz Kurgan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lukasz Kurgan. The network helps show where Lukasz Kurgan may publish in the future.

Co-authorship network of co-authors of Lukasz Kurgan

This figure shows the co-authorship network connecting the top 25 collaborators of Lukasz Kurgan. A scholar is included among the top collaborators of Lukasz Kurgan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lukasz Kurgan. Lukasz Kurgan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Song, Jiangning & Lukasz Kurgan. (2025). Two decades of advances in sequence-based prediction of MoRFs, disorder-to-order transitioning binding regions. Expert Review of Proteomics. 22(1). 1–9. 1 indexed citations

Basu, Sushmita, Yuedong Yang, & Lukasz Kurgan. (2025). Prediction of nucleic acid binding residues in protein sequences: Recent advances and future prospects. Current Opinion in Structural Biology. 94. 103085–103085.

Bi, Yue, et al.. (2024). SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations. Genomics Proteomics & Bioinformatics. 22(6). 3 indexed citations

Basu, Sushmita, Jing Yu, Daisuke Kihara, & Lukasz Kurgan. (2024). Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences. Briefings in Bioinformatics. 26(1). 2 indexed citations

Hu, Gang, et al.. (2024). flDPnn2: Accurate and Fast Predictor of Intrinsic Disorder in Proteins. Journal of Molecular Biology. 436(17). 168605–168605. 9 indexed citations

Zhang, Fuhao & Lukasz Kurgan. (2024). Evaluation of predictions of disordered binding regions in the CAID2 experiment. Computational and Structural Biotechnology Journal. 27. 78–88.

Zhang, Jian, Jingjing Qian, Quan Zou, Feng Zhou, & Lukasz Kurgan. (2024). Recent Advances in Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences. Methods in molecular biology. 1–19. 2 indexed citations

Basu, Sushmita, Tamás Hegedűs, & Lukasz Kurgan. (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). 168272–168272. 7 indexed citations

Zhang, Jian, Sushmita Basu, & Lukasz Kurgan. (2023). HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins. Nucleic Acids Research. 52(2). e10–e10. 21 indexed citations

10.

Wang, Meng, Lukasz Kurgan, & Min Li. (2023). A comprehensive assessment and comparison of tools for HLA class I peptide-binding prediction. Briefings in Bioinformatics. 24(3). 13 indexed citations

11.

Kurgan, Lukasz, Gang Hu, Kui Wang, et al.. (2023). Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins. Nature Protocols. 18(11). 3157–3172. 21 indexed citations

12.

Basu, Sushmita, Bi Zhao, Eshel Faraggi, et al.. (2023). DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options. Nucleic Acids Research. 52(D1). D426–D433. 7 indexed citations

13.

Chen, Zhen, Xuhan Liu, Pei Zhao, et al.. (2022). iFeatureOmega:an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets. Nucleic Acids Research. 50(W1). W434–W447. 57 indexed citations

14.

Hu, Gang, Akila Katuwawala, Kui Wang, et al.. (2021). flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions. Nature Communications. 12(1). 4438–4438. 180 indexed citations

15.

Peng, Zhenling, et al.. (2021). Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues. Biomolecules. 11(9). 1337–1337.

16.

Yan, Jing, et al.. (2013). RAPID: Fast and accurate sequence-based prediction of intrinsic disorder content on proteomic scale. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1834(8). 1671–1680. 51 indexed citations

17.

Zhang, Hua, Tuo Zhang, Ke Chen, et al.. (2008). Sequence based residue depth prediction using evolutionary information and predicted secondary structure. BMC Bioinformatics. 9(1). 388–388. 36 indexed citations

18.

Ruan, Jishou, Ke Chen, Jack A. Tuszyński, & Lukasz Kurgan. (2006). Quantitative Analysis of the Conservation of the Tertiary Structure of Protein Segments. The Protein Journal. 25(5). 301–315. 5 indexed citations

19.

Kurgan, Lukasz & Krzysztof J. Cios. (2003). Fast Class-Attribute Interdependence Maximization (CAIM) Discretization Algorithm.. 30–36. 15 indexed citations

20.

Kurgan, Lukasz, Krzysztof J. Cios, Ryszard Tadeusiewicz, Marek R. Ogiela, & L.S. Goodenday. (2001). Knowledge discovery approach to automated cardiac SPECT diagnosis. Artificial Intelligence in Medicine. 23(2). 149–169. 171 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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