Nataša Pržulj
- Molecular Biology top 1%
- Computational Theory and Mathematics top 0.2%
- Statistical and Nonlinear Physics top 0.5%
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Co-authors
- Igor JurišicaTijana MilenkovićNoël Malod‐DogninOleksii KuchaievDerek G. CorneilVladimir GligorijevićAndrew D. KingVesna Memišević
- Topics
- Bioinformatics and Genomic Networks (78 papers)Computational Drug Discovery Methods (32 papers)Microbial Metabolic Engineering and Bioproduction (26 papers)
- Partner nations
- United KingdomUnited StatesSpain
In The Last Decade
Nataša Pržulj
91 papers receiving 5.8k citations
Hit Papers
Peers
Comparison fields: 5 of 180
- Molecular Biology 4.4k
- Computational Theory and Mathematics 1.5k
- Statistical and Nonlinear Physics 1.0k
- Artificial Intelligence 669
- Computer Vision and Pattern Recognition 347
Countries citing papers authored by Nataša Pržulj
This map shows the geographic impact of Nataša Pržulj'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 Nataša Pržulj with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nataša Pržulj more than expected).
Fields of papers citing papers by Nataša Pržulj
This network shows the impact of papers produced by Nataša Pržulj. 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 Nataša Pržulj. The network helps show where Nataša Pržulj may publish in the future.
Co-authorship network of co-authors of Nataša Pržulj
This figure shows the co-authorship network connecting the top 25 collaborators of Nataša Pržulj. A scholar is included among the top collaborators of Nataša Pržulj 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 Nataša Pržulj. Nataša Pržulj is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 8 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 16 | |
| 7 | 185 | |
| 8 | 5 | |
| 9 | 36 | |
| 10 | 64 | |
| 11 | 77 | |
| 12 | 13 | |
| 13 | 28 | |
| 14 | An integrative approach to modelling biological networks | 1 |
| 15 | 136 | |
| 16 | 116 | |
| 17 | 74 | |
| 18 | 66 | |
| 19 | High-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cellsbreakdown → | 553 |
| 20 | 8 |
About Nataša Pržulj
Nataša Pržulj is a scholar working on Computational Theory and Mathematics, Molecular Biology and Statistical and Nonlinear Physics, having authored 92 papers that have together received 6.0k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (78 papers), Computational Drug Discovery Methods (32 papers) and Microbial Metabolic Engineering and Bioproduction (26 papers). The work is most often cited by research in Computational Theory and Mathematics (1.5k citations), Statistical and Nonlinear Physics (1.0k citations) and Molecular Biology (4.4k citations). Nataša Pržulj has collaborated with scholars based in United Kingdom, United States and Spain. Frequent co-authors include Igor Jurišica, Tijana Milenković, Noël Malod‐Dognin, Oleksii Kuchaiev, Derek G. Corneil, Vladimir Gligorijević, Andrew D. King, Vesna Memišević, Wayne B. Hayes and Dennis A. Wigle. Their work appears in journals such as Science, Proceedings of the National Academy of Sciences 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.