Yaron Orenstein

1.5k total citations
48 papers, 830 citations indexed

About

Yaron Orenstein is a scholar working on Molecular Biology, Artificial Intelligence and Ecology. According to data from OpenAlex, Yaron Orenstein has authored 48 papers receiving a total of 830 indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 6 papers in Artificial Intelligence and 3 papers in Ecology. Recurrent topics in Yaron Orenstein's work include RNA and protein synthesis mechanisms (27 papers), Genomics and Chromatin Dynamics (16 papers) and RNA Research and Splicing (14 papers). Yaron Orenstein is often cited by papers focused on RNA and protein synthesis mechanisms (27 papers), Genomics and Chromatin Dynamics (16 papers) and RNA Research and Splicing (14 papers). Yaron Orenstein collaborates with scholars based in Israel, United States and France. Yaron Orenstein's co-authors include Ron Shamir, Bonnie Berger, Benny Chor, Yuhao Wang, David Pellow, Carl Kingsford, Guillaume Marçais, Yimeng Yin, Jussi Taipale and Remo Rohs 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

Yaron Orenstein

45 papers receiving 820 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yaron Orenstein Israel 16 760 66 61 55 53 48 830
Chaoyang Zhang United States 14 496 0.7× 68 1.0× 73 1.2× 46 0.8× 48 0.9× 43 651
Matthew D. Edwards United States 9 753 1.0× 35 0.5× 114 1.9× 75 1.4× 62 1.2× 11 860
Iván Dotú United States 16 878 1.2× 42 0.6× 68 1.1× 26 0.5× 65 1.2× 33 1.0k
Cécile Pereira United States 8 355 0.5× 100 1.5× 43 0.7× 55 1.0× 73 1.4× 10 506
Yingbo Cui China 8 422 0.6× 25 0.4× 54 0.9× 45 0.8× 30 0.6× 26 511
Matthew Ruffalo United States 8 258 0.3× 34 0.5× 77 1.3× 40 0.7× 76 1.4× 16 333
Fritz Lekschas United States 10 348 0.5× 42 0.6× 63 1.0× 94 1.7× 19 0.4× 16 498
Martin Vingron Germany 9 649 0.9× 116 1.8× 90 1.5× 63 1.1× 27 0.5× 14 803
Pavel P. Kuksa United States 13 422 0.6× 95 1.4× 71 1.2× 43 0.8× 104 2.0× 37 532
Bin Duan China 11 534 0.7× 40 0.6× 51 0.8× 28 0.5× 52 1.0× 19 631

Countries citing papers authored by Yaron Orenstein

Since Specialization
Citations

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

Fields of papers citing papers by Yaron Orenstein

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yaron Orenstein

This figure shows the co-authorship network connecting the top 25 collaborators of Yaron Orenstein. A scholar is included among the top collaborators of Yaron Orenstein 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 Yaron Orenstein. Yaron Orenstein 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.
Orenstein, Yaron, et al.. (2024). Deep neural networks for predicting the affinity landscape of protein-protein interactions. iScience. 27(9). 110772–110772. 1 indexed citations
2.
Orenstein, Yaron, et al.. (2023). Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks. Frontiers in Cell and Developmental Biology. 11. 1034604–1034604. 2 indexed citations
3.
Orenstein, Yaron, et al.. (2023). An overview on nucleic-acid G-quadruplex prediction: from rule-based methods to deep neural networks. Briefings in Bioinformatics. 24(4). 6 indexed citations
4.
Pellow, David, et al.. (2023). Efficient minimizer orders for large values of k using minimum decycling sets. Genome Research. 33(7). 1154–1161. 7 indexed citations
5.
Orenstein, Yaron, et al.. (2023). SWOffinder: Efficient and versatile search of CRISPR off-targets with bulges by Smith-Waterman alignment. iScience. 27(1). 108557–108557. 1 indexed citations
6.
Orenstein, Yaron, et al.. (2022). A systematic evaluation of data processing and problem formulation of CRISPR off-target site prediction. Briefings in Bioinformatics. 23(5). 13 indexed citations
7.
Sal‐Man, Neta, et al.. (2022). Predicting the pathogenicity of bacterial genomes using widely spread protein families. BMC Bioinformatics. 23(1). 253–253. 5 indexed citations
8.
Orenstein, Yaron, et al.. (2021). Improving the efficiency of de Bruijn graph construction using compact universal hitting sets. 1–9. 2 indexed citations
9.
Shimko, Tyler C., Polly M. Fordyce, & Yaron Orenstein. (2020). DeCoDe: degenerate codon design for complete protein-coding DNA libraries. Bioinformatics. 36(11). 3357–3364. 7 indexed citations
10.
Tillo, Desiree, Robert E. Boer, Nima Assad, et al.. (2020). Custom DNA Microarrays Reveal Diverse Binding Preferences of Proteins and Small Molecules to Thousands of G-Quadruplexes. ACS Chemical Biology. 15(4). 925–935. 41 indexed citations
11.
Berger, Bonnie, et al.. (2020). A Randomized Parallel Algorithm for Efficiently Finding Near-Optimal Universal Hitting Sets. Lecture notes in computer science. 12074. 37–53. 17 indexed citations
12.
Chor, Benny, et al.. (2018). A deep neural network approach for learning intrinsic protein-RNA binding preferences. Bioinformatics. 34(17). i638–i646. 65 indexed citations
13.
Le, Daniel D., Tyler C. Shimko, Arjun K. Aditham, et al.. (2018). Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding. Proceedings of the National Academy of Sciences. 115(16). E3702–E3711. 51 indexed citations
14.
Marçais, Guillaume, et al.. (2017). Improving the performance of minimizers and winnowing schemes. Bioinformatics. 33(14). i110–i117. 43 indexed citations
15.
Orenstein, Yaron, David Pellow, Guillaume Marçais, Ron Shamir, & Carl Kingsford. (2017). Designing small universal k-mer hitting sets for improved analysis of high-throughput sequencing. PLoS Computational Biology. 13(10). e1005777–e1005777. 29 indexed citations
16.
Yang, Lin, Yaron Orenstein, Arttu Jolma, et al.. (2017). Transcription factor family‐specific DNA shape readout revealed by quantitative specificity models. Molecular Systems Biology. 13(2). 910–910. 86 indexed citations
17.
Orenstein, Yaron, Dorit Avrahami, Chaim Wachtel, et al.. (2016). SELMAP - SELEX affinity landscape MAPping of transcription factor binding sites using integrated microfluidics. Scientific Reports. 6(1). 33351–33351. 13 indexed citations
18.
Glick, Yaïr, Yaron Orenstein, Diana Ideses, et al.. (2014). Drosophila TRF2 is a preferential core promoter regulator. Genes & Development. 28(19). 2163–2174. 40 indexed citations
19.
Orenstein, Yaron & Ron Shamir. (2013). Design of shortest double-stranded DNA sequences covering all k-mers with applications to protein-binding microarrays and synthetic enhancers. Bioinformatics. 29(13). i71–i79. 8 indexed citations
20.
Orenstein, Yaron & Dana Ron. (2011). Testing Eulerianity and connectivity in directed sparse graphs. Theoretical Computer Science. 412(45). 6390–6408. 4 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Chaoyang Zhang Breakdown of academic impact, for papers by Matthew D. Edwards Breakdown of academic impact, for papers by Iván Dotú Breakdown of academic impact, for papers by Cécile Pereira Breakdown of academic impact, for papers by Yingbo Cui Breakdown of academic impact, for papers by Matthew Ruffalo Breakdown of academic impact, for papers by Fritz Lekschas Breakdown of academic impact, for papers by Martin Vingron Breakdown of academic impact, for papers by Pavel P. Kuksa Breakdown of academic impact, for papers by Bin Duan
Rankless by CCL
2026