Liat Ein‐Dor
Impact in
- Cancer Research top 5%
- Breast Cancer Treatment Studies
- Molecular Biology top 10%
- Gene expression and cancer classification
- Bioinformatics and Genomic Networks
Papers in
-
- Topic Modeling 8
- Natural Language Processing Techniques 5
- Neural Networks and Applications 3
- Sentiment Analysis and Opinion Mining 3
- Machine Learning and Algorithms 3
- Co-authors
- Eytan Domany (5 shared papers)Or Zuk (2 shared papers)David Givol (3 shared papers)Gad A. Getz (2 shared papers)Itai Kela (2 shared papers)Noam Slonim (11 shared papers)Ido Kanter (7 shared papers)Ariel Gera (8 shared papers)
- Journals
- Europhysics Letters (EPL) (1 paper)Nature (1 paper)Breast Cancer Research (1 paper)Computer applications in the biosciences (1 paper)IBM Journal of Research and Development (1 paper)
- Partner nations
- IsraelUnited StatesGermany
In The Last Decade
Liat Ein‐Dor
30 papers receiving 1.9k citations
Peers
Comparison fields: 5 of 117
- Cancer Research 308
- Molecular Biology 901
- Artificial Intelligence 357
- Hematology 90
- Statistics and Probability 72
Countries citing papers authored by Liat Ein‐Dor
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
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-authors
The 25 scholars most cited alongside Liat Ein‐Dor, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2004 | 627 | |
| 2 | 2006 | 545 | |
| 3 | 2006 | 129 | |
| 4 | 2005 | 123 | |
| 5 | 2020 | 116 | |
| 6 | 2021 | 110 | |
| 7 | 2014 | 68 | |
| 8 | 2022 | 38 | |
| 9 | 2020 | 32 | |
| 10 | 2005 | 27 | |
| 11 | 2001 | 17 | |
| 12 | 2019 | 15 | |
| 13 | 2023 | 13 | |
| 14 | 2001 | 12 | |
| 15 | 1999 | 10 | |
| 16 | 2001 | 9 | |
| 17 | 2002 | 9 | |
| 18 | 2007 | 9 | |
| 19 | 2022 | 9 | |
| 20 | 1998 | 6 |
About Liat Ein‐Dor
Liat Ein‐Dor is a scholar working on Artificial Intelligence, Condensed Matter Physics, Cancer Research, Information Systems and Management Science and Operations Research, having authored 31 papers that have together received 1.9k indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Natural Language Processing Techniques (5 papers), Neural Networks and Applications (3 papers), Sentiment Analysis and Opinion Mining (3 papers), Gene expression and cancer classification (3 papers), Machine Learning and Algorithms (3 papers), Information Retrieval and Search Behavior (2 papers) and Breast Cancer Treatment Studies (2 papers). The work is most often cited by research in Cancer Research (308 citations), Molecular Biology (901 citations), Artificial Intelligence (357 citations), Hematology (90 citations) and Statistics and Probability (72 citations). Liat Ein‐Dor has collaborated with scholars based in Israel, United States and Germany. Frequent co-authors include Eytan Domany, Or Zuk, David Givol, Gad A. Getz, Itai Kela, Noam Slonim, Ido Kanter, Ariel Gera, Alon Halfon and Ilan Tsafrir. Their work appears in journals such as Europhysics Letters (EPL), Nature, Breast Cancer Research, Computer applications in the biosciences and IBM Journal of Research and Development.
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.