Lukasz Kurgan
- Molecular Biology top 0.5%
- Artificial Intelligence top 0.5%
- Materials Chemistry top 2%
- Computational Theory and Mathematics top 0.2%
- Information Systems top 1%
- Co-authors
- Vladimir N. UverskyMarcin J. MiziantyKe ChenWitold PedryczZhenling PengKrzysztof J. CiosWojciech StachBin Xue
- Topics
- Protein Structure and Dynamics (139 papers)Machine Learning in Bioinformatics (104 papers)RNA and protein synthesis mechanisms (84 papers)
- Partner nations
- CanadaUnited StatesChina
In The Last Decade
Lukasz Kurgan
246 papers receiving 11.9k citations
Hit Papers
Peers
Comparison fields: 5 of 192
- Molecular Biology 8.6k
- Artificial Intelligence 1.9k
- Materials Chemistry 1.9k
- Computational Theory and Mathematics 1.4k
- Information Systems 568
Countries citing papers authored by Lukasz Kurgan
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 9 | |
| 4 | 0 | |
| 5 | 7 | |
| 6 | 21 | |
| 7 | 7 | |
| 8 | 57 | |
| 9 | 0 | |
| 10 | 180 | |
| 11 | 26 | |
| 12 | 31 | |
| 13 | 22 | |
| 14 | 122 | |
| 15 | 51 | |
| 16 | 94 | |
| 17 | 36 | |
| 18 | Fast Class-Attribute Interdependence Maximization (CAIM) Discretization Algorithm. | 15 |
| 19 | Semantic Mapping of XML Tags using Inductive Machine Learning | 23 |
| 20 | 171 |
About Lukasz Kurgan
Lukasz Kurgan is a scholar working on Molecular Biology, Computational Theory and Mathematics and Filtration and Separation, having authored 255 papers that have together received 12.2k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (139 papers), Machine Learning in Bioinformatics (104 papers) and RNA and protein synthesis mechanisms (84 papers). The work is most often cited by research in Molecular Biology (8.6k citations), Computational Theory and Mathematics (1.4k citations) and Artificial Intelligence (1.9k citations). Lukasz Kurgan has collaborated with scholars based in Canada, United States and China. Frequent 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. Their work appears in journals such as Chemical Reviews, Nucleic Acids Research and Nature Communications.
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.