Dana Pe’er
- Biophysics top 0.05%
- Cell Image Analysis Techniques 15
- Immunology top 0.2%
- T-cell and B-cell Immunology 14
- Immune Cell Function and Interaction 12
- Molecular Biology top 0.1%
- Single-cell and spatial transcriptomics 47
- Gene Regulatory Network Analysis 19
- Gene expression and cancer classification 16
- Bioinformatics and Genomic Networks 15
- Cancer Research top 0.5%
- Cancer Genomics and Diagnostics 19
- Oncology top 0.2%
- Co-authors
- Nir FriedmanGarry P. NolanIftach NachmanMichal LinialSean C. BendallErin F. SimondsJacob LevineKaren Sachs
- Partner nations
- United StatesIsraelCanada
In The Last Decade
Dana Pe’er
117 papers receiving 22.8k citations
Hit Papers
Peers
Comparison fields: 5 of 200
- Biophysics 2.0k
- Immunology 6.1k
- Molecular Biology 14.7k
- Cancer Research 2.9k
- Oncology 5.0k
Countries citing papers authored by Dana Pe’er
This map shows the geographic impact of Dana Pe’er'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 Dana Pe’er with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dana Pe’er more than expected).
Fields of papers citing papers by Dana Pe’er
This network shows the impact of papers produced by Dana Pe’er. 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 Dana Pe’er. The network helps show where Dana Pe’er may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Dana Pe’er, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 8 | |
| 2 | 2025 | 0 | |
| 3 | CellRank 2: unified fate mapping in multiview single-cell databreakdown → | 2024 | 49 |
| 4 | 2023 | 7 | |
| 5 | 2023 | 26 | |
| 6 | CellRank for directed single-cell fate mappingbreakdown → | 2022 | 282 |
| 7 | 2022 | 11 | |
| 8 | 2021 | 145 | |
| 9 | 2021 | 73 | |
| 10 | Regenerative lineages and immune-mediated pruning in lung cancer metastasisbreakdown → | 2020 | 251 |
| 11 | 2020 | 170 | |
| 12 | 2019 | 251 | |
| 13 | 2018 | 33 | |
| 14 | 2018 | 12 | |
| 15 | 2016 | 302 | |
| 16 | 2014 | 43 | |
| 17 | 2014 | 139 | |
| 18 | 2013 | 44 | |
| 19 | Normalization of mass cytometry data with bead standardsbreakdown → | 2013 | 462 |
| 20 | MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals | 2006 | 22 |
About Dana Pe’er
Dana Pe’er is a scholar working on Biophysics, Cancer Research, Immunology, Molecular Biology and Oncology, having authored 120 papers that have together received 23.2k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (47 papers), Cancer Genomics and Diagnostics (19 papers), Gene Regulatory Network Analysis (19 papers), Gene expression and cancer classification (16 papers), Cell Image Analysis Techniques (15 papers), Bioinformatics and Genomic Networks (15 papers), T-cell and B-cell Immunology (14 papers) and Immune Cell Function and Interaction (12 papers). The work is most often cited by research in Biophysics (2.0k citations), Immunology (6.1k citations), Molecular Biology (14.7k citations), Cancer Research (2.9k citations) and Oncology (5.0k citations). Dana Pe’er has collaborated with scholars based in United States, Israel and Canada. Frequent co-authors include Nir Friedman, Garry P. Nolan, Iftach Nachman, Michal Linial, Sean C. Bendall, Erin F. Simonds, Jacob Levine, Karen Sachs, Michelle D. Tadmor and El-ad David Amir. Their work appears in journals such as Cell, Nature Biotechnology, Cancer Research, Proceedings of the National Academy of Sciences and Nature.
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.