Chanita Kuseva

755 total citations
20 papers, 582 citations indexed

About

Chanita Kuseva is a scholar working on Computational Theory and Mathematics, Small Animals and Dermatology. According to data from OpenAlex, Chanita Kuseva has authored 20 papers receiving a total of 582 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computational Theory and Mathematics, 9 papers in Small Animals and 6 papers in Dermatology. Recurrent topics in Chanita Kuseva's work include Computational Drug Discovery Methods (11 papers), Animal testing and alternatives (9 papers) and Pesticide Exposure and Toxicity (6 papers). Chanita Kuseva is often cited by papers focused on Computational Drug Discovery Methods (11 papers), Animal testing and alternatives (9 papers) and Pesticide Exposure and Toxicity (6 papers). Chanita Kuseva collaborates with scholars based in Bulgaria, United States and France. Chanita Kuseva's co-authors include Ovanes Mekenyan, Todor Pavlov, T.W. Schultz, Robert Diderich, Atanas Chapkanov, Grace Patlewicz, Tomasz Sobański, David W. Roberts, S. Dimitrov and Nevena Todorova and has published in prestigious journals such as Chemical Research in Toxicology, Regulatory Toxicology and Pharmacology and Methods in molecular biology.

In The Last Decade

Chanita Kuseva

19 papers receiving 566 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chanita Kuseva Bulgaria 14 262 164 151 118 104 20 582
Todor Pavlov Bulgaria 15 440 1.7× 148 0.9× 205 1.4× 95 0.8× 80 0.8× 22 804
Jay Russell Niemelä Denmark 10 294 1.1× 106 0.6× 186 1.2× 151 1.3× 58 0.6× 16 631
Elisabet Berggren Italy 10 116 0.4× 199 1.2× 318 2.1× 134 1.1× 92 0.9× 23 688
Steve Gutsell United Kingdom 15 255 1.0× 122 0.7× 216 1.4× 36 0.3× 61 0.6× 33 649
Catherine Mahony United Kingdom 17 156 0.6× 266 1.6× 255 1.7× 93 0.8× 139 1.3× 36 922
Claire M. Ellison United Kingdom 11 242 0.9× 77 0.5× 158 1.0× 39 0.3× 59 0.6× 14 469
Worth Andrew 15 152 0.6× 94 0.6× 183 1.2× 41 0.3× 65 0.6× 36 449
Martina Klarić Belgium 17 122 0.5× 318 1.9× 201 1.3× 296 2.5× 127 1.2× 31 800
Jason Yarbrough United States 12 84 0.3× 70 0.4× 104 0.7× 57 0.5× 88 0.8× 21 562
Arianna Bassan Italy 13 245 0.9× 71 0.4× 135 0.9× 15 0.1× 57 0.5× 30 501

Countries citing papers authored by Chanita Kuseva

Since Specialization
Citations

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

Fields of papers citing papers by Chanita Kuseva

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chanita Kuseva

This figure shows the co-authorship network connecting the top 25 collaborators of Chanita Kuseva. A scholar is included among the top collaborators of Chanita Kuseva 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 Chanita Kuseva. Chanita Kuseva 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.
2.
Kuseva, Chanita, et al.. (2022). Predicting explosive properties of chemicals accounting for thermodynamic and kinetic factors. Computational Toxicology. 23. 100230–100230. 3 indexed citations
3.
Kuseva, Chanita, et al.. (2021). Criteria for quantitative assessment of metabolic similarity between chemicals. II. Application to human health endpoints. Computational Toxicology. 19. 100173–100173. 5 indexed citations
4.
Schultz, T.W., et al.. (2021). Assessing metabolic similarity for read-across predictions. Computational Toxicology. 18. 100160–100160. 13 indexed citations
5.
Schultz, T.W., et al.. (2021). The QSAR Toolbox automated read-across workflow for predicting acute oral toxicity: II. Verification and validation. Computational Toxicology. 20. 100194–100194. 17 indexed citations
6.
Dimitrova, Gergana, et al.. (2020). Modeling hazard assessment of chemicals based on adducts formation. I. A basis for inclusion of kinetic factors in simulating skin sensitization. Computational Toxicology. 15. 100130–100130. 6 indexed citations
7.
Schultz, T.W., Chanita Kuseva, Todor Pavlov, et al.. (2019). Automated and standardized workflows in the OECD QSAR Toolbox. Computational Toxicology. 10. 89–104. 34 indexed citations
8.
Kuseva, Chanita, Todor Pavlov, Atanas Chapkanov, et al.. (2019). Using metabolic information for categorization and read-across in the OECD QSAR Toolbox. Computational Toxicology. 12. 100102–100102. 16 indexed citations
9.
Kuseva, Chanita, T.W. Schultz, Todor Pavlov, et al.. (2019). The implementation of RAAF in the OECD QSAR Toolbox. Regulatory Toxicology and Pharmacology. 105. 51–61. 19 indexed citations
10.
Kuseva, Chanita, T.W. Schultz, Todor Pavlov, et al.. (2019). Category consistency in the OECD QSAR Toolbox: Assessment and reporting tool to justify read-across. Computational Toxicology. 11. 65–71. 15 indexed citations
11.
Schultz, T.W., Robert Diderich, Chanita Kuseva, & Ovanes Mekenyan. (2018). The OECD QSAR Toolbox Starts Its Second Decade. Methods in molecular biology. 1800. 55–77. 69 indexed citations
12.
Schultz, T.W., Chanita Kuseva, Todor Pavlov, et al.. (2018). Alert performance: A new functionality in the OECD QSAR Toolbox. Computational Toxicology. 10. 26–37. 15 indexed citations
13.
Petkov, P., Chanita Kuseva, Stefan Kotov, et al.. (2017). Procedure for toxicological predictions based on mechanistic weight of evidences: Application to Ames mutagenicity. Computational Toxicology. 12. 100009–100009. 2 indexed citations
14.
Dimitrov, S., Ann Detroyer, Charles Gomes, et al.. (2016). Accounting for data variability, a key factor in in vivo/in vitro relationships: application to the skin sensitization potency (in vivo LLNA versus in vitro DPRA) example. Journal of Applied Toxicology. 36(12). 1568–1578. 17 indexed citations
15.
Dimitrov, S., Robert Diderich, Tomasz Sobański, et al.. (2016). QSAR Toolbox – workflow and major functionalities. SAR and QSAR in environmental research. 27(3). 203–219. 175 indexed citations
16.
Patlewicz, Grace, Chanita Kuseva, Gergana Dimitrova, et al.. (2014). TIMES-SS – Recent refinements resulting from an industrial skin sensitisation consortium. SAR and QSAR in environmental research. 25(5). 367–391. 32 indexed citations
17.
Patlewicz, Grace, et al.. (2014). Towards AOP application – Implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitization. Regulatory Toxicology and Pharmacology. 69(3). 529–545. 83 indexed citations
18.
Mekenyan, Ovanes, Grace Patlewicz, Chanita Kuseva, et al.. (2014). A Mechanistic Approach to Modeling Respiratory Sensitization. Chemical Research in Toxicology. 27(2). 219–239. 33 indexed citations
19.
Patlewicz, Grace, Ovanes Mekenyan, Gergana Dimitrova, et al.. (2010). Can mutagenicity information be useful in an Integrated Testing Strategy (ITS) for skin sensitization?. SAR and QSAR in environmental research. 21(7-8). 619–656. 9 indexed citations
20.
Mekenyan, Ovanes, Grace Patlewicz, Gergana Dimitrova, et al.. (2010). Use of Genotoxicity Information in the Development of Integrated Testing Strategies (ITS) for Skin Sensitization. Chemical Research in Toxicology. 23(10). 1519–1540. 19 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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