Jasmina Bogojeska

861 total citations
24 papers, 409 citations indexed

About

Jasmina Bogojeska is a scholar working on Computer Networks and Communications, Artificial Intelligence and Information Systems. According to data from OpenAlex, Jasmina Bogojeska has authored 24 papers receiving a total of 409 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Networks and Communications, 8 papers in Artificial Intelligence and 7 papers in Information Systems. Recurrent topics in Jasmina Bogojeska's work include HIV Research and Treatment (6 papers), Machine Learning and Algorithms (4 papers) and Software System Performance and Reliability (4 papers). Jasmina Bogojeska is often cited by papers focused on HIV Research and Treatment (6 papers), Machine Learning and Algorithms (4 papers) and Software System Performance and Reliability (4 papers). Jasmina Bogojeska collaborates with scholars based in Switzerland, Germany and United States. Jasmina Bogojeska's co-authors include D. Wiesmann, Thomas Lengauer, Ioana Giurgiu, Steffen Bickel, Tobias Scheffer, André Altmann, Maurizio Zazzi, Volker Röth, Sonali Parbhoo and Finale Doshi‐Velez and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and The Journal of Infectious Diseases.

In The Last Decade

Jasmina Bogojeska

23 papers receiving 397 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jasmina Bogojeska Switzerland 10 193 133 86 50 39 24 409
Robin Senge Germany 11 306 1.6× 23 0.2× 91 1.1× 61 1.2× 35 0.9× 18 531
Jacek Koronacki Poland 12 172 0.9× 16 0.1× 43 0.5× 178 3.6× 22 0.6× 30 523
Lopamudra Dey India 10 209 1.1× 25 0.2× 85 1.0× 91 1.8× 31 0.8× 19 442
Suresh Kumar India 13 221 1.1× 100 0.8× 170 2.0× 20 0.4× 10 0.3× 72 505
S. Kannan United States 10 339 1.8× 115 0.9× 122 1.4× 82 1.6× 25 0.6× 33 518
Ujjwal Maulik India 9 76 0.4× 13 0.1× 20 0.2× 117 2.3× 20 0.5× 24 288
Christelle Vangenot Switzerland 8 100 0.5× 121 0.9× 61 0.7× 29 0.6× 4 0.1× 25 583
Lijun Cai China 12 135 0.7× 36 0.3× 40 0.5× 54 1.1× 21 0.5× 42 365
Ananda Theertha Suresh United States 15 624 3.2× 121 0.9× 56 0.7× 27 0.5× 10 0.3× 45 744

Countries citing papers authored by Jasmina Bogojeska

Since Specialization
Citations

This map shows the geographic impact of Jasmina Bogojeska'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 Jasmina Bogojeska with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jasmina Bogojeska more than expected).

Fields of papers citing papers by Jasmina Bogojeska

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jasmina Bogojeska. 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 Jasmina Bogojeska. The network helps show where Jasmina Bogojeska may publish in the future.

Co-authorship network of co-authors of Jasmina Bogojeska

This figure shows the co-authorship network connecting the top 25 collaborators of Jasmina Bogojeska. A scholar is included among the top collaborators of Jasmina Bogojeska 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 Jasmina Bogojeska. Jasmina Bogojeska 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.
Bogojeska, Jasmina, et al.. (2025). GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation. Information. 16(7). 524–524. 3 indexed citations
2.
Weber, Anna, et al.. (2024). Do domain-specific protein language models outperform general models on immunology-related tasks?. SHILAP Revista de lepidopterología. 14. 100036–100036. 4 indexed citations
3.
Bogojeska, Jasmina, et al.. (2023). Reinforced Active Learning for Low-Resource, Domain-Specific, Multi-Label Text Classification. Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences). 3 indexed citations
4.
Marzolini, Catia, Andri Rauch, Huldrych F. Günthard, et al.. (2020). Cohort-Derived Machine Learning Models for Individual Prediction of Chronic Kidney Disease in People Living With Human Immunodeficiency Virus: A Prospective Multicenter Cohort Study. The Journal of Infectious Diseases. 224(7). 1198–1208. 6 indexed citations
5.
Bogojeska, Jasmina & D. Wiesmann. (2018). Transfer learning for server behavior classification in small IT environments. 8. 1–9. 2 indexed citations
6.
Parbhoo, Sonali, Jasmina Bogojeska, Maurizio Zazzi, Volker Röth, & Finale Doshi‐Velez. (2017). Combining Kernel and Model Based Learning for HIV Therapy Selection. PubMed. 2017. 239–248. 37 indexed citations
7.
Giurgiu, Ioana, et al.. (2016). Predicting Disk Replacement towards Reliable Data Centers. 39–48. 117 indexed citations
8.
Giurgiu, Ioana, et al.. (2014). Analysis of Labor Efforts and their Impact Factors to Solve Server Incidents in Datacenters. 8. 424–433. 8 indexed citations
9.
Bogojeska, Jasmina, et al.. (2014). Impact of HW and OS type and currency on server availability derived from problem ticket analysis. 1–9. 10 indexed citations
10.
Schneider, Johannes, Jasmina Bogojeska, & Michail Vlachos. (2014). Solving Linear SVMs with Multiple 1D Projections. IRIS. 221–230. 1 indexed citations
11.
Maksai, Andrii, Jasmina Bogojeska, & D. Wiesmann. (2014). Hierarchical Incident Ticket Classification with Minimal Supervision. 3. 923–928. 7 indexed citations
12.
Bogojeska, Jasmina, et al.. (2013). Classifying server behavior and predicting impact of modernization actions. 59–66. 20 indexed citations
13.
Bogojeska, Jasmina & Thomas Lengauer. (2012). Hierarchical Bayes Model for Predicting Effectiveness of HIV Combination Therapies. Statistical Applications in Genetics and Molecular Biology. 11(3). Article 11–Article 11. 7 indexed citations
14.
Bogojeska, Jasmina, Daniel Stöckel, Maurizio Zazzi, et al.. (2012). History-alignment Models for Bias-aware Prediction of Virological Response to HIV Combination Therapy. MPG.PuRe (Max Planck Society). 118–126. 5 indexed citations
15.
Bogojeska, Jasmina. (2011). History distribution matching method for predicting effectiveness of HIV combination therapies. Max Planck Institute for Plasma Physics. 24. 424–432. 1 indexed citations
16.
Saigo, Hiroto, André Altmann, Jasmina Bogojeska, et al.. (2011). Learning from Past Treatments and Their Outcome Improves Prediction of In Vivo Response to Anti-HIV Therapy. Statistical Applications in Genetics and Molecular Biology. 10(1). Article 6–Article 6. 17 indexed citations
17.
Bogojeska, Jasmina, Steffen Bickel, André Altmann, & Thomas Lengauer. (2010). Dealing with sparse data in predicting outcomes of HIV combination therapies. Bioinformatics. 26(17). 2085–2092. 16 indexed citations
18.
Bogojeska, Jasmina, Thomas Lengauer, & Jörg Rahnenführer. (2008). Stability analysis of mixtures of mutagenetic trees. BMC Bioinformatics. 9(1). 165–165. 8 indexed citations
19.
Bogojeska, Jasmina, Adrian Alexa, André Altmann, Thomas Lengauer, & Jörg Rahnenführer. (2008). Rtreemix: an R package for estimating evolutionary pathways and genetic progression scores. Bioinformatics. 24(20). 2391–2392. 12 indexed citations
20.
Bickel, Steffen, Jasmina Bogojeska, Thomas Lengauer, & Tobias Scheffer. (2008). Multi-task learning for HIV therapy screening. Max Planck Institute for Plasma Physics. 56–63. 103 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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