Yuval Benjamini

2.9k total citations · 2 hit papers
16 papers, 1.6k citations indexed

About

Yuval Benjamini is a scholar working on Molecular Biology, Sensory Systems and Genetics. According to data from OpenAlex, Yuval Benjamini has authored 16 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 3 papers in Sensory Systems and 3 papers in Genetics. Recurrent topics in Yuval Benjamini's work include Olfactory and Sensory Function Studies (3 papers), Genetic Syndromes and Imprinting (2 papers) and Genetic Associations and Epidemiology (2 papers). Yuval Benjamini is often cited by papers focused on Olfactory and Sensory Function Studies (3 papers), Genetic Syndromes and Imprinting (2 papers) and Genetic Associations and Epidemiology (2 papers). Yuval Benjamini collaborates with scholars based in Israel, United States and Australia. Yuval Benjamini's co-authors include Terence P. Speed, Jack L. Gallant, Thomas Naselaris, An T. Vu, Bin Yu, Shinji Nishimoto, Ronen Kadmon, Rafael A. Irizarry, Sutirtha Chakraborty and Keegan Korthauer and has published in prestigious journals such as Nucleic Acids Research, Neuron and SHILAP Revista de lepidopterología.

In The Last Decade

Yuval Benjamini

15 papers receiving 1.6k citations

Hit Papers

Summarizing and correcting the GC content bias in high-th... 2011 2026 2016 2021 2012 2011 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yuval Benjamini Israel 10 705 442 301 229 188 16 1.6k
Alfredo Rodríguez United States 19 752 1.1× 348 0.8× 215 0.7× 64 0.3× 82 0.4× 40 2.2k
John Thorson United States 27 602 0.9× 350 0.8× 350 1.2× 82 0.4× 86 0.5× 66 2.5k
Dimitri Perrin Australia 13 884 1.3× 287 0.6× 133 0.4× 57 0.2× 65 0.3× 47 2.2k
Duncan Donohue United States 19 1.0k 1.5× 266 0.6× 164 0.5× 295 1.3× 58 0.3× 34 2.1k
John M. Gaspar United States 19 581 0.8× 589 1.3× 173 0.6× 93 0.4× 127 0.7× 31 1.6k
Menachem Fromer United States 21 1.2k 1.6× 121 0.3× 897 3.0× 201 0.9× 116 0.6× 37 2.1k
Julien Prados Switzerland 20 1.5k 2.1× 158 0.4× 374 1.2× 261 1.1× 60 0.3× 40 2.5k
Neil Weisenfeld United States 15 545 0.8× 146 0.3× 339 1.1× 27 0.1× 324 1.7× 18 1.5k
Zhaoyang Feng United States 24 865 1.2× 59 0.1× 103 0.3× 176 0.8× 114 0.6× 40 2.6k
Jesse Gillis United States 30 2.2k 3.2× 417 0.9× 578 1.9× 180 0.8× 301 1.6× 72 3.1k

Countries citing papers authored by Yuval Benjamini

Since Specialization
Citations

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

Fields of papers citing papers by Yuval Benjamini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuval Benjamini

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

All Works

16 of 16 papers shown
1.
Abulafia, Adi Shacham, David Zadok, Thomas Kohnen, et al.. (2024). Spherical equivalent prediction analysis in intraocular lens power calculations using Eyetemis: a comprehensive approach. Journal of Cataract & Refractive Surgery. 50(11). 1128–1134. 11 indexed citations
2.
Benjamini, Yuval, et al.. (2024). A Review of Veridical Data Science by Bin Yu and Rebecca L. Barter. SHILAP Revista de lepidopterología. 6(2).
3.
Sorkin, Nir, et al.. (2024). Comparison of Intraocular Lens Power Prediction Accuracy Between 2 Swept-Source Optical Coherence Tomography Biometry Devices. American Journal of Ophthalmology. 265. 156–164. 2 indexed citations
4.
Abulafia, Adi, Tos T. J. M. Berendschot, Yuval Benjamini, et al.. (2023). Accuracy of Toric Intraocular Lens Calculations Using Estimated Versus Measured Posterior Corneal Astigmatism. American Journal of Ophthalmology. 262. 107–113. 2 indexed citations
5.
Margulis, Eitan, et al.. (2022). BitterMatch: recommendation systems for matching molecules with bitter taste receptors. Journal of Cheminformatics. 14(1). 45–45. 20 indexed citations
6.
Ayalon, Liat, et al.. (2022). Gender homophily and gender distribution in social networks: The case of older adults in long term care settings. Social Networks. 71. 70–79. 1 indexed citations
7.
Karni, Noam, Yuval Benjamini, Ran Nir‐Paz, et al.. (2021). Onset, duration and unresolved symptoms, including smell and taste changes, in mild COVID-19 infection: a cohort study in Israeli patients. Clinical Microbiology and Infection. 27(5). 769–774. 57 indexed citations
8.
Steinsland, Ingelin, Miina Ollikainen, Jaakko Kaprio, et al.. (2021). Modeling dependency structures in 450k DNA methylation data. Bioinformatics. 38(4). 885–891. 2 indexed citations
9.
Karni, Noam, Yuval Benjamini, Sarah Israel, et al.. (2020). Self-Rated Smell Ability Enables Highly Specific Predictors of COVID-19 Status: A Case–Control Study in Israel. Open Forum Infectious Diseases. 8(2). ofaa589–ofaa589. 22 indexed citations
10.
Benjamini, Yuval, Jonathan Taylor, & Rafael A. Irizarry. (2018). Selection-Corrected Statistical Inference for Region Detection With High-Throughput Assays. Journal of the American Statistical Association. 114(527). 1351–1365. 4 indexed citations
11.
Korthauer, Keegan, Sutirtha Chakraborty, Yuval Benjamini, & Rafael A. Irizarry. (2018). Detection and accurate false discovery rate control of differentially methylated regions from whole genome bisulfite sequencing. Biostatistics. 20(3). 367–383. 100 indexed citations
12.
Evrony, Gilad D., Eun‐Jung Lee, Bhaven K. Mehta, et al.. (2015). Cell Lineage Analysis in Human Brain Using Endogenous Retroelements. Neuron. 85(1). 49–59. 184 indexed citations
13.
Chandrananda, Dineika, Natalie Thorne, Devika Ganesamoorthy, et al.. (2014). Investigating and Correcting Plasma DNA Sequencing Coverage Bias to Enhance Aneuploidy Discovery. PLoS ONE. 9(1). e86993–e86993. 21 indexed citations
14.
Benjamini, Yuval & Terence P. Speed. (2012). Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Research. 40(10). e72–e72. 580 indexed citations breakdown →
15.
Nishimoto, Shinji, An T. Vu, Thomas Naselaris, et al.. (2011). Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies. Current Biology. 21(19). 1641–1646. 528 indexed citations breakdown →
16.
Kadmon, Ronen & Yuval Benjamini. (2006). Effects of Productivity and Disturbance on Species Richness: A Neutral Model. The American Naturalist. 167(6). 939–946. 68 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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