Tomer Tsaban

1.5k total citations · 1 hit paper
7 papers, 357 citations indexed

About

Tomer Tsaban is a scholar working on Molecular Biology, Genetics and Oncology. According to data from OpenAlex, Tomer Tsaban has authored 7 papers receiving a total of 357 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 2 papers in Genetics and 1 paper in Oncology. Recurrent topics in Tomer Tsaban's work include Machine Learning in Bioinformatics (3 papers), Ubiquitin and proteasome pathways (3 papers) and Protein Structure and Dynamics (2 papers). Tomer Tsaban is often cited by papers focused on Machine Learning in Bioinformatics (3 papers), Ubiquitin and proteasome pathways (3 papers) and Protein Structure and Dynamics (2 papers). Tomer Tsaban collaborates with scholars based in Israel, United Kingdom and New Zealand. Tomer Tsaban's co-authors include Ora Schueler‐Furman, Orly Avraham, Ziv Ben-Aharon, Julia K. Varga, Alisa Khramushin, Yifat Merbl, Lea Eisenbach, Yishai Levin, Guo Ci Teo and Matthias P. Kramer and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Tomer Tsaban

7 papers receiving 354 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Harnessing protein folding neural networks for peptide–protein docking

2022 211 citations Tomer Tsaban, Julia K. Varga et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tomer Tsaban Israel	6	278	61	42	37	36	7	357
Vincent Frappier Canada	10	304 1.1×	25 0.4×	14 0.3×	48 1.3×	51 1.4×	12	376
Mileidy W. Gonzalez United States	6	324 1.2×	35 0.6×	34 0.8×	64 1.7×	11 0.3×	8	408
Yuquan Tong United States	11	790 2.8×	67 1.1×	32 0.8×	33 0.9×	15 0.4×	18	872
Steven De Munck Belgium	7	184 0.7×	22 0.4×	51 1.2×	25 0.7×	21 0.6×	7	274
Elizaveta A. Kovrigina United States	8	387 1.4×	24 0.4×	23 0.5×	33 0.9×	52 1.4×	12	424
Galen Dods United States	6	430 1.5×	30 0.5×	15 0.4×	14 0.4×	33 0.9×	8	500
T. Scott Chen United States	7	278 1.0×	28 0.5×	20 0.5×	30 0.8×	22 0.6×	7	326
Fruzsina Hóbor United Kingdom	11	451 1.6×	35 0.6×	16 0.4×	19 0.5×	16 0.4×	18	501
Mikail D. Levasseur Switzerland	7	158 0.6×	29 0.5×	26 0.6×	21 0.6×	26 0.7×	12	280
Claudio Mirabello Sweden	14	428 1.5×	23 0.4×	72 1.7×	108 2.9×	97 2.7×	23	526

Countries citing papers authored by Tomer Tsaban

Since Specialization

Citations

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

Fields of papers citing papers by Tomer Tsaban

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tomer Tsaban

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

All Works

Sort: Min cites: Since: Top N: Style:

7 of 7 papers shown

1.

Avraham, Orly, et al.. (2023). Protein language models can capture protein quaternary state. BMC Bioinformatics. 24(1). 433–433. 14 indexed citations

2.

Khramushin, Alisa, Ziv Ben-Aharon, Tomer Tsaban, et al.. (2022). Matching protein surface structural patches for high-resolution blind peptide docking. Proceedings of the National Academy of Sciences. 119(18). e2121153119–e2121153119. 18 indexed citations

3.

Rotem‐Bamberger, Shahar, Keren Keinan‐Adamsky, Tomer Tsaban, et al.. (2022). Structural insights into the role of the WW2 domain on tandem WW–PPxY motif interactions of oxidoreductase WWOX. Journal of Biological Chemistry. 298(8). 102145–102145. 4 indexed citations

4.

Kacen, Assaf, Aaron Javitt, Matthias P. Kramer, et al.. (2022). Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors. Nature Biotechnology. 41(2). 239–251. 67 indexed citations

5.

Tsaban, Tomer, Julia K. Varga, Orly Avraham, et al.. (2022). Harnessing protein folding neural networks for peptide–protein docking. Nature Communications. 13(1). 176–176. 211 indexed citations breakdown →

6.

Tsaban, Tomer, et al.. (2021). CladeOScope: functional interactions through the prism of clade-wise co-evolution. NAR Genomics and Bioinformatics. 3(2). lqab024–lqab024. 18 indexed citations

7.

Tsaban, Tomer, Einav Cohen‐Kfir, Moshe Dessau, et al.. (2021). Structural basis for UFM1 transfer from UBA5 to UFC1. Nature Communications. 12(1). 5708–5708. 25 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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