Nataša Pržulj

12.2k total citations · 1 hit paper
92 papers, 6.0k citations indexed

About

Nataša Pržulj is a scholar working on Molecular Biology, Computational Theory and Mathematics and Statistical and Nonlinear Physics. According to data from OpenAlex, Nataša Pržulj has authored 92 papers receiving a total of 6.0k indexed citations (citations by other indexed papers that have themselves been cited), including 85 papers in Molecular Biology, 35 papers in Computational Theory and Mathematics and 11 papers in Statistical and Nonlinear Physics. Recurrent topics in Nataša Pržulj's work include Bioinformatics and Genomic Networks (78 papers), Computational Drug Discovery Methods (32 papers) and Microbial Metabolic Engineering and Bioproduction (26 papers). Nataša Pržulj is often cited by papers focused on Bioinformatics and Genomic Networks (78 papers), Computational Drug Discovery Methods (32 papers) and Microbial Metabolic Engineering and Bioproduction (26 papers). Nataša Pržulj collaborates with scholars based in United Kingdom, United States and Spain. Nataša Pržulj's co-authors include Igor Jurišica, Tijana Milenković, Noël Malod‐Dognin, Oleksii Kuchaiev, Derek G. Corneil, Andrew D. King, Vladimir Gligorijević, Vesna Memišević, Wayne B. Hayes and Dennis A. Wigle and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Nataša Pržulj

91 papers receiving 5.8k citations

Hit Papers

High-Throughput Mapping o... 2005 2026 2012 2019 2005 100 200 300 400 500
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.

  1. High-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cells
    2005 553 citations Miriam Barrios‐Rodiles, Kevin R. Brown et al. Science profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nataša Pržulj United Kingdom 37 4.4k 1.5k 1.0k 669 347 92 6.0k
Hiroshi Mamitsuka Japan 35 2.7k 0.6× 1.0k 0.7× 170 0.2× 1.1k 1.7× 391 1.1× 180 4.3k
Marinka Žitnik United States 30 2.9k 0.7× 1.5k 1.0× 284 0.3× 1.8k 2.7× 325 0.9× 84 6.1k
Limsoon Wong Singapore 46 4.7k 1.1× 1.2k 0.8× 295 0.3× 1.7k 2.6× 257 0.7× 303 7.8k
Min Li China 53 7.3k 1.7× 2.8k 1.9× 289 0.3× 927 1.4× 354 1.0× 489 10.8k
Reinhard Schneider Germany 43 7.0k 1.6× 915 0.6× 181 0.2× 368 0.6× 231 0.7× 196 10.5k
Roded Sharan Israel 57 11.3k 2.6× 3.0k 2.0× 399 0.4× 1.1k 1.6× 349 1.0× 214 14.0k
Jihong Guan China 34 2.2k 0.5× 986 0.7× 932 0.9× 788 1.2× 293 0.8× 292 4.5k
Ali Masoudi‐Nejad Iran 34 2.4k 0.5× 961 0.6× 213 0.2× 330 0.5× 104 0.3× 165 3.7k
Doheon Lee South Korea 38 4.1k 0.9× 925 0.6× 89 0.1× 873 1.3× 404 1.2× 229 6.9k
Ming Hao United States 26 2.4k 0.6× 577 0.4× 127 0.1× 452 0.7× 560 1.6× 126 5.1k

Countries citing papers authored by Nataša Pržulj

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Malod‐Dognin, Noël, et al.. (2024). MONFIT: multi-omics factorization-based integration of time-series data sheds light on Parkinson’s disease. PubMed. 1(4). ugae012–ugae012. 2 indexed citations
2.
Malod‐Dognin, Noël, et al.. (2023). A functional analysis of omic network embedding spaces reveals key altered functions in cancer. Bioinformatics. 39(5). 1 indexed citations
3.
Malod‐Dognin, Noël, et al.. (2023). Integrated Data Analysis Uncovers New COVID-19 Related Genes and Potential Drug Re-Purposing Candidates. International Journal of Molecular Sciences. 24(2). 1431–1431. 1 indexed citations
4.
Cifuentes, Myriam Patricia, Noël Malod‐Dognin, Paul D. Juárez, et al.. (2022). Big Data to Knowledge Analytics Reveals the Zika Virus Epidemic as Only One of Multiple Factors Contributing to a Year-Over-Year 28-Fold Increase in Microcephaly Incidence. International Journal of Environmental Research and Public Health. 19(15). 9051–9051. 2 indexed citations
5.
Malod‐Dognin, Noël, et al.. (2021). Linear functional organization of the omic embedding space. Bioinformatics. 37(21). 3839–3847. 3 indexed citations
6.
Malod‐Dognin, Noël, et al.. (2020). Unveiling new disease, pathway, and gene associations via multi-scale neural network. PLoS ONE. 15(4). e0231059–e0231059. 12 indexed citations
7.
Perin, Nataša, Irena Sović, Nataša Pržulj, et al.. (2019). Antiproliferative activity and mode of action analysis of novel amino and amido substituted phenantrene and naphtho[2,1-b]thiophene derivatives. European Journal of Medicinal Chemistry. 185. 111833–111833. 16 indexed citations
8.
Yaveroğlu, Ömer Nebil, Noël Malod‐Dognin, D. Davis, et al.. (2014). Revealing the Hidden Language of Complex Networks. Scientific Reports. 4(1). 4547–4547. 139 indexed citations
9.
Janjić, Vuk & Nataša Pržulj. (2014). The Topology of the Growing Human Interactome Data. Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics. 11(2). 27–42. 3 indexed citations
10.
Janjić, Vuk & Nataša Pržulj. (2012). The Core Diseasome. Molecular BioSystems. 8(10). 2614–2625. 36 indexed citations
11.
Janjić, Vuk & Nataša Pržulj. (2012). Biological function through network topology: a survey of the human diseasome. Briefings in Functional Genomics. 11(6). 522–532. 40 indexed citations
12.
Milenković, Tijana, Vesna Memišević, Anthony Bonato, & Nataša Pržulj. (2011). Dominating Biological Networks. PLoS ONE. 6(8). e23016–e23016. 77 indexed citations
13.
Memišević, Vesna, Tijana Milenković, & Nataša Pržulj. (2010). An integrative approach to modeling biological networks. SHILAP Revista de lepidopterología. 13 indexed citations
14.
Pržulj, Nataša & Oleksii Kuchaiev. (2010). Modeling and alignment of biological networks. 193–193. 1 indexed citations
15.
Memišević, Vesna, Tijana Milenković, & Nataša Pržulj. (2009). An integrative approach to modelling biological networks. arXiv (Cornell University). 1 indexed citations
16.
Milenković, Tijana, et al.. (2008). Characterization of the proteasome interaction network using a QTAX-based tag-team strategy and protein interaction network analysis. Proceedings of the National Academy of Sciences. 105(36). 13333–13338. 116 indexed citations
17.
Milenković, Tijana, Jih‐Sheng Lai, & Nataša Pržulj. (2008). GraphCrunch: A tool for large network analyses. BMC Bioinformatics. 9(1). 70–70. 74 indexed citations
18.
Pržulj, Nataša, Derek G. Corneil, & Igor Jurišica. (2006). Efficient estimation of graphlet frequency distributions in protein–protein interaction networks. Bioinformatics. 22(8). 974–980. 85 indexed citations
19.
Barrios‐Rodiles, Miriam, Kevin R. Brown, Barish Ozdamar, et al.. (2005). High-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cells. Science. 307(5715). 1621–1625. 553 indexed citations breakdown →
20.
King, Andrew D., Nataša Pržulj, & Igor Jurišica. (2004). Protein complex prediction via cost-based clustering. Bioinformatics. 20(17). 3013–3020. 464 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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