Liat Ein‐Dor

2.6k total citations · 2 hit papers
29 papers, 1.6k citations indexed

About

Liat Ein‐Dor is a scholar working on Artificial Intelligence, Molecular Biology and Statistical and Nonlinear Physics. According to data from OpenAlex, Liat Ein‐Dor has authored 29 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 6 papers in Molecular Biology and 5 papers in Statistical and Nonlinear Physics. Recurrent topics in Liat Ein‐Dor's work include Topic Modeling (8 papers), Natural Language Processing Techniques (8 papers) and Neural Networks and Applications (6 papers). Liat Ein‐Dor is often cited by papers focused on Topic Modeling (8 papers), Natural Language Processing Techniques (8 papers) and Neural Networks and Applications (6 papers). Liat Ein‐Dor collaborates with scholars based in Israel, United States and Germany. Liat Ein‐Dor's co-authors include Eytan Domany, Or Zuk, David Givol, Itai Kela, Gad Getz, Ido Kanter, Noam Slonim, Dafna Tsafrir, Ilan Tsafrir and Michal Ozery-Flato and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Bioinformatics.

In The Last Decade

Liat Ein‐Dor

25 papers receiving 1.6k citations

Hit Papers

Outcome signature genes in breast cancer: is there a uniq... 2004 2026 2011 2018 2004 2006 100 200 300 400 500
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.

  1. Outcome signature genes in breast cancer: is there a unique set?
    2004 580 citations Liat Ein‐Dor, Itai Kela et al. Bioinformatics profile →
  2. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer
    2006 506 citations Liat Ein‐Dor, Or Zuk et al. Proceedings of the National Academy of Sciences profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Liat Ein‐Dor Israel 11 1.1k 307 268 140 114 29 1.6k
Yu‐Chiao Chiu United States 23 1.1k 0.9× 540 1.8× 169 0.6× 127 0.9× 142 1.2× 65 1.5k
Emek Demir United States 22 1.8k 1.5× 323 1.1× 86 0.3× 121 0.9× 320 2.8× 53 2.1k
Daniele Ramazzotti Italy 17 813 0.7× 380 1.2× 179 0.7× 106 0.8× 34 0.3× 54 1.3k
Xinghua Lu United States 28 1.3k 1.1× 350 1.1× 423 1.6× 383 2.7× 132 1.2× 151 2.4k
Miguél Vázquez Spain 20 1.0k 0.9× 288 0.9× 232 0.9× 169 1.2× 150 1.3× 65 1.9k
Özgün Babur United States 20 1.6k 1.4× 398 1.3× 74 0.3× 308 2.2× 275 2.4× 42 2.0k
Tane S. Ray United States 12 1.1k 1.0× 193 0.6× 336 1.3× 374 2.7× 47 0.4× 31 2.2k
Lorraine Tanabe United States 13 1.9k 1.7× 155 0.5× 831 3.1× 220 1.6× 190 1.7× 19 2.3k
Kim Last United Kingdom 4 1.1k 1.0× 200 0.7× 331 1.2× 455 3.3× 41 0.4× 6 2.0k

Countries citing papers authored by Liat Ein‐Dor

Since Specialization
Citations

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

Fields of papers citing papers by Liat Ein‐Dor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Liat Ein‐Dor. 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 Liat Ein‐Dor. The network helps show where Liat Ein‐Dor may publish in the future.

Co-authorship network of co-authors of Liat Ein‐Dor

This figure shows the co-authorship network connecting the top 25 collaborators of Liat Ein‐Dor. A scholar is included among the top collaborators of Liat Ein‐Dor 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 Liat Ein‐Dor. Liat Ein‐Dor 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.
Gera, Ariel, Liat Ein‐Dor, Eyal Shnarch, et al.. (2024). Efficient Benchmarking (of Language Models). 2519–2536. 4 indexed citations
2.
Gera, Ariel, et al.. (2024). Label-Efficient Model Selection for Text Generation. 8384–8402.
3.
Gera, Ariel, et al.. (2023). Active Learning for Natural Language Generation. 9862–9877.
4.
Gera, Ariel, et al.. (2022). Zero-Shot Text Classification with Self-Training. 1107–1119. 27 indexed citations
5.
Aharonov, Ranit, et al.. (2022). Quality Controlled Paraphrase Generation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 596–609. 10 indexed citations
6.
Ein‐Dor, Liat, Alon Halfon, Ariel Gera, et al.. (2020). Active Learning for BERT: An Empirical Study. 7949–7962. 79 indexed citations
7.
Ein‐Dor, Liat, Ariel Gera, Orith Toledo‐Ronen, et al.. (2019). Financial Event Extraction Using Wikipedia-Based Weak Supervision. 10–15. 12 indexed citations
8.
Ozery-Flato, Michal, Liat Ein‐Dor, Ranit Aharonov, et al.. (2016). Identifying and Investigating Unexpected Response to Treatment: A Diabetes Case Study. Big Data. 4(3). 148–159. 6 indexed citations
9.
Ein‐Dor, Liat, et al.. (2013). Analytics for resiliency in the mainframe. IBM Journal of Research and Development. 57(5). 8:1–8:5.
10.
Zuk, Or, Liat Ein‐Dor, & Eytan Domany. (2012). Ranking Under Uncertainty.. Uncertainty in Artificial Intelligence. 466–473. 1 indexed citations
11.
Gal, Hilah, Ninette Amariglio, L. Trakhtenbrot, et al.. (2006). Gene expression profiles of AML derived stem cells; similarity to hematopoietic stem cells. Leukemia. 20(12). 2147–2154. 108 indexed citations
12.
Ein‐Dor, Liat, Or Zuk, & Eytan Domany. (2006). Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proceedings of the National Academy of Sciences. 103(15). 5923–5928. 506 indexed citations breakdown →
13.
Tsafrir, Dafna, et al.. (2005). Sorting points into neighborhoods (SPIN): data analysis and visualization by ordering distance matrices. Computer applications in the biosciences. 21(10). 2301–2308. 113 indexed citations
14.
Ein‐Dor, Liat, Itai Kela, Gad Getz, David Givol, & Eytan Domany. (2004). Outcome signature genes in breast cancer: is there a unique set?. Bioinformatics. 21(2). 171–178. 580 indexed citations breakdown →
15.
Ein‐Dor, Liat, Ido Kanter, & Wolfgang Kinzel. (2002). Low autocorrelated multiphase sequences. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 65(2). 20102–20102. 8 indexed citations
16.
Ein‐Dor, Liat, Richard Metzler, Ido Kanter, & Wolfgang Kinzel. (2001). Multichoice minority game. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 63(6). 66103–66103. 17 indexed citations
17.
Metzler, Richard, Wolfgang Kinzel, Liat Ein‐Dor, & Ido Kanter. (2001). Generation of unpredictable time series by a neural network. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 63(5). 6 indexed citations
18.
Ein‐Dor, Liat, Richard Metzler, Ido Kanter, & Wolfgang Kinzel. (2000). Multi-Choices Minority Game. arXiv (Cornell University). 1 indexed citations
19.
Kanter, Ido, et al.. (2000). Secure and linear cryptosystems using error-correcting codes. Europhysics Letters (EPL). 51(2). 244–244. 2 indexed citations
20.
Ein‐Dor, Liat & Ido Kanter. (1999). Confidence in prediction by neural networks. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 60(1). 799–802. 10 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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