Pazit Polak

2.7k total citations
34 papers, 2.0k citations indexed

About

Pazit Polak is a scholar working on Molecular Biology, Immunology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Pazit Polak has authored 34 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 10 papers in Immunology and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Pazit Polak's work include T-cell and B-cell Immunology (9 papers), Immune Cell Function and Interaction (7 papers) and Monoclonal and Polyclonal Antibodies Research (6 papers). Pazit Polak is often cited by papers focused on T-cell and B-cell Immunology (9 papers), Immune Cell Function and Interaction (7 papers) and Monoclonal and Polyclonal Antibodies Research (6 papers). Pazit Polak collaborates with scholars based in Israel, United States and United Kingdom. Pazit Polak's co-authors include Michael N. Hall, Markus A. Rüegg, Nadine Cybulski, Johan Auwerx, Jérôme N. Feige, Orit Shefi, Francesca Trapani, Marion Cornu, Markus H. Heim and Charles Betz and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.

In The Last Decade

Pazit Polak

32 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pazit Polak Israel 17 1.4k 361 309 265 180 34 2.0k
Sushma Nagaraja Grellscheid United Kingdom 17 976 0.7× 446 1.2× 160 0.5× 243 0.9× 68 0.4× 28 1.6k
Christopher B. Jackson Finland 23 1.3k 0.9× 225 0.6× 180 0.6× 188 0.7× 151 0.8× 53 1.9k
Aaron M. Robitaille United States 16 1.6k 1.1× 165 0.5× 139 0.4× 188 0.7× 139 0.8× 20 2.0k
Heather R. Keys United States 13 1.7k 1.3× 164 0.5× 199 0.6× 177 0.7× 78 0.4× 17 2.3k
Lixin Hong China 17 1.1k 0.8× 294 0.8× 363 1.2× 169 0.6× 81 0.5× 19 1.8k
Yu‐Sheng Cong China 28 1.6k 1.1× 843 2.3× 239 0.8× 192 0.7× 102 0.6× 64 2.5k
K. Yoshino Japan 6 1.5k 1.1× 186 0.5× 266 0.9× 222 0.8× 233 1.3× 7 1.9k
Brian C. Capell United States 24 2.5k 1.8× 562 1.6× 272 0.9× 460 1.7× 159 0.9× 44 3.4k
Roberta Mannucci Italy 26 1.3k 1.0× 253 0.7× 358 1.2× 91 0.3× 148 0.8× 38 2.1k
Gary J. Litherland United Kingdom 21 1.1k 0.8× 442 1.2× 149 0.5× 254 1.0× 249 1.4× 42 2.1k

Countries citing papers authored by Pazit Polak

Since Specialization
Citations

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

Fields of papers citing papers by Pazit Polak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pazit Polak

This figure shows the co-authorship network connecting the top 25 collaborators of Pazit Polak. A scholar is included among the top collaborators of Pazit Polak 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 Pazit Polak. Pazit Polak 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.
Kozák, Josef, et al.. (2025). Approximation of the soil particle-size distribution curve using a NURBS curve. Soil and Water Research. 20(1). 16–31. 1 indexed citations
2.
Lees, William, Ayelet Peres, N. Amos, et al.. (2025). The current landscape of adaptive immune receptor genomic and repertoire data: OGRDB and VDJbase. Nucleic Acids Research. 54(D1). D932–D937.
3.
Peres, Ayelet, et al.. (2024). An unbiased comparison of immunoglobulin sequence aligners. Briefings in Bioinformatics. 25(6). 2 indexed citations
4.
Peres, Ayelet, William Lees, Oscar L. Rodriguez, et al.. (2023). IGHV allele similarity clustering improves genotype inference from adaptive immune receptor repertoire sequencing data. Nucleic Acids Research. 51(16). e86–e86. 12 indexed citations
5.
Safra, Modi, Pazit Polak, Shachaf Shiber, et al.. (2023). Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity. Frontiers in Immunology. 14. 1031914–1031914. 9 indexed citations
6.
Peres, Ayelet, Oscar L. Rodriguez, Corey T. Watson, et al.. (2022). T cell receptor beta germline variability is revealed by inference from repertoire data. Genome Medicine. 14(1). 2–2. 29 indexed citations
7.
Polak, Pazit, et al.. (2021). Immune2vec: Embedding B/T Cell Receptor Sequences in ℝN Using Natural Language Processing. Frontiers in Immunology. 12. 680687–680687. 27 indexed citations
8.
Gidoni, Moriah, Omri Snir, Ayelet Peres, et al.. (2019). Mosaic deletion patterns of the human antibody heavy chain gene locus shown by Bayesian haplotyping. Nature Communications. 10(1). 628–628. 67 indexed citations
9.
Vigneault, François, Chris Clouser, Nimer Assy, et al.. (2018). Antibody Repertoire Analysis of Hepatitis C Virus Infections Identifies Immune Signatures Associated With Spontaneous Clearance. Frontiers in Immunology. 9. 3004–3004. 26 indexed citations
10.
Ilovitsh, Asaf, Pazit Polak, Zeev Zalevsky, & Orit Shefi. (2016). Selective inactivation of enzymes conjugated to nanoparticles using tuned laser illumination. Cytometry Part A. 91(8). 767–774. 5 indexed citations
11.
Kraus, Bettina J., Juliano L. Sartoretto, Pazit Polak, et al.. (2015). Novel role for retinol-binding protein 4 in the regulation of blood pressure. The FASEB Journal. 29(8). 3133–3140. 34 indexed citations
12.
Polak, Pazit & Orit Shefi. (2015). Nanometric agents in the service of neuroscience: Manipulation of neuronal growth and activity using nanoparticles. Nanomedicine Nanotechnology Biology and Medicine. 11(6). 1467–1479. 55 indexed citations
13.
Polak, Pazit, et al.. (2014). EVALUATION OF QUALITY ACCEPTANCE OF COWS MEAT IN RESPECT TO CERTAIN SOCIAL ASPECTS OF CONSUMERS. Slovak Journal of Animal Science. 47(2). 105–110. 1 indexed citations
14.
Polak, Pazit, et al.. (2013). Thermal Degradation of DNA. DNA and Cell Biology. 32(6). 298–301. 120 indexed citations
15.
Banin, Ehud, et al.. (2013). The Synergistic Effect of Visible Light and Gentamycin on <em>Pseudomona aeruginosa</em> Microorganisms. Journal of Visualized Experiments. e4370–e4370. 8 indexed citations
16.
Hagiwara, Asami, Marion Cornu, Nadine Cybulski, et al.. (2012). Hepatic mTORC2 Activates Glycolysis and Lipogenesis through Akt, Glucokinase, and SREBP1c. Cell Metabolism. 15(5). 725–738. 450 indexed citations
17.
Polak, Pazit & Michael N. Hall. (2009). mTOR and the control of whole body metabolism. Current Opinion in Cell Biology. 21(2). 209–218. 246 indexed citations
18.
Polak, Pazit, Nadine Cybulski, Jérôme N. Feige, et al.. (2008). Adipose-Specific Knockout of raptor Results in Lean Mice with Enhanced Mitochondrial Respiration. Cell Metabolism. 8(5). 399–410. 395 indexed citations
19.
Thedieck, Kathrin, Pazit Polak, Adiel Cohen, et al.. (2007). PRAS40 and PRR5-Like Protein Are New mTOR Interactors that Regulate Apoptosis. PLoS ONE. 2(11). e1217–e1217. 223 indexed citations
20.
Vardimon, Lily, et al.. (2006). Cytoskeletal and cell contact control of the glucocorticoid pathway. Molecular and Cellular Endocrinology. 252(1-2). 142–147. 9 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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