Jeff Kiralis

781 total citations
10 papers, 470 citations indexed

About

Jeff Kiralis is a scholar working on Genetics, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Jeff Kiralis has authored 10 papers receiving a total of 470 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Genetics, 5 papers in Molecular Biology and 2 papers in Artificial Intelligence. Recurrent topics in Jeff Kiralis's work include Genetic Associations and Epidemiology (7 papers), Bioinformatics and Genomic Networks (5 papers) and Genetic Mapping and Diversity in Plants and Animals (4 papers). Jeff Kiralis is often cited by papers focused on Genetic Associations and Epidemiology (7 papers), Bioinformatics and Genomic Networks (5 papers) and Genetic Mapping and Diversity in Plants and Animals (4 papers). Jeff Kiralis collaborates with scholars based in United States, Canada and Denmark. Jeff Kiralis's co-authors include Jason H. Moore, Nicholas A. Sinnott‐Armstrong, Ryan J. Urbanowicz, Jonathan Fisher, Casey S. Greene, Nadia M. Penrod, Ting Hu, Angeline S. Andrew, Margaret R. Karagas and Ting Hu and has published in prestigious journals such as BMC Bioinformatics, Journal of the American Medical Informatics Association and Transactions of the American Mathematical Society.

In The Last Decade

Jeff Kiralis

9 papers receiving 465 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jeff Kiralis United States 7 335 303 69 33 19 10 470
Alexandros Kanterakis Greece 9 152 0.5× 105 0.3× 66 1.0× 19 0.6× 18 0.9× 32 371
Anastasia Lucas United States 11 188 0.6× 202 0.7× 29 0.4× 36 1.1× 11 0.6× 24 417
Aidan N. Gomez Canada 2 257 0.8× 164 0.5× 58 0.8× 11 0.3× 10 0.5× 3 442
Yuri Pirola Italy 10 223 0.7× 75 0.2× 64 0.9× 20 0.6× 15 0.8× 30 321
Joseph Min United States 5 352 1.1× 185 0.6× 22 0.3× 12 0.4× 11 0.6× 6 520
Michal Ozery-Flato Israel 10 136 0.4× 88 0.3× 96 1.4× 18 0.5× 10 0.5× 22 347
Eugene van Someren Netherlands 6 491 1.5× 82 0.3× 40 0.6× 11 0.3× 32 1.7× 11 667
Mafalda Dias United Kingdom 1 257 0.8× 164 0.5× 22 0.3× 11 0.3× 10 0.5× 2 375
Minzhu Xie China 11 313 0.9× 149 0.5× 41 0.6× 11 0.3× 59 3.1× 38 463
Xingjie Shi China 14 353 1.1× 156 0.5× 18 0.3× 20 0.6× 11 0.6× 32 516

Countries citing papers authored by Jeff Kiralis

Since Specialization
Citations

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

Fields of papers citing papers by Jeff Kiralis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jeff Kiralis

This figure shows the co-authorship network connecting the top 25 collaborators of Jeff Kiralis. A scholar is included among the top collaborators of Jeff Kiralis 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 Jeff Kiralis. Jeff Kiralis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
1.
Urbanowicz, Ryan J., et al.. (2014). A classification and characterization of two-locus, pure, strict, epistatic models for simulation and detection. BioData Mining. 7(1). 8–8. 6 indexed citations
2.
Moore, Jason H., et al.. (2014). Heuristic Identification of Biological Architectures for Simulating Complex Hierarchical Genetic Interactions. Genetic Epidemiology. 39(1). 25–34. 6 indexed citations
3.
Hu, Ting, Yuanzhu Chen, Jeff Kiralis, et al.. (2013). An information-gain approach to detecting three-way epistatic interactions in genetic association studies. Journal of the American Medical Informatics Association. 20(4). 630–636. 56 indexed citations
4.
Hu, Ting, Yuanzhu Chen, Jeff Kiralis, & Jason H. Moore. (2013). ViSEN: Methodology and Software for Visualization of Statistical Epistasis Networks. Genetic Epidemiology. 37(3). 283–285. 33 indexed citations
5.
Urbanowicz, Ryan J., et al.. (2012). GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures. BioData Mining. 5(1). 16–16. 151 indexed citations
6.
Urbanowicz, Ryan J., Jeff Kiralis, Jonathan Fisher, & Jason H. Moore. (2012). Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection. BioData Mining. 5(1). 15–15. 23 indexed citations
7.
Hu, Ting, Nicholas A. Sinnott‐Armstrong, Jeff Kiralis, et al.. (2011). Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinformatics. 12(1). 364–364. 93 indexed citations
8.
Greene, Casey S., Nadia M. Penrod, Jeff Kiralis, & Jason H. Moore. (2009). Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions. BioData Mining. 2(1). 5–5. 98 indexed citations
9.
10.
Kiralis, Jeff. (1992). Pseudo-Isotopies of Irreducible 3-Manifolds. Transactions of the American Mathematical Society. 332(1). 53–53. 1 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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