Peyton Greenside
Impact in
- Cancer Research top 5%
- Cancer Genomics and Diagnostics
- Molecular Biology top 5%
- Genomics and Chromatin Dynamics
- Single-cell and spatial transcriptomics
- Epigenetics and DNA Methylation
- RNA Research and Splicing
- RNA modifications and cancer
- CRISPR and Genetic Engineering
- RNA and protein synthesis mechanisms
Papers in
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- Cancer Genomics and Diagnostics 2
-
- Genomics and Chromatin Dynamics 5
- RNA Research and Splicing 3
- CRISPR and Genetic Engineering 2
- Co-authors
- Anshul KundajeM SnyderJonathan K. PritchardWilliam J. GreenleafM. Ryan CorcesRavindra MajetiJason D. BuenrostroJulie L. Koenig
- Journals
- Cancer Discovery (1 paper)Nature Genetics (1 paper)PLoS Biology (1 paper)Nature Medicine (1 paper)CPT Pharmacometrics & Systems Pharmacology (1 paper)
- Partner nations
- United StatesGermanyAustria
In The Last Decade
Peyton Greenside
13 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 97
- Cancer Research 312
- Molecular Biology 1.3k
- Hematology 148
- Immunology 204
- Oncology 238
Countries citing papers authored by Peyton Greenside
This map shows the geographic impact of Peyton Greenside'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 Peyton Greenside with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peyton Greenside more than expected).
Fields of papers citing papers by Peyton Greenside
This network shows the impact of papers produced by Peyton Greenside. 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 Peyton Greenside. The network helps show where Peyton Greenside may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Peyton Greenside, 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 | 2020 | 113 | |
| 2 | 2019 | 59 | |
| 3 | 2018 | 51 | |
| 4 | 2018 | 104 | |
| 5 | 2018 | 2 | |
| 6 | 2017 | 128 | |
| 7 | 2017 | 7 | |
| 8 | 2017 | 111 | |
| 9 | 2017 | 78 | |
| 10 | Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution Hit paper breakdown → | 2016 | 656 |
| 11 | 2016 | 85 | |
| 12 | 2015 | 212 | |
| 13 | 2015 | 44 |
About Peyton Greenside
Peyton Greenside is a scholar working on Cancer Research, Molecular Biology, Computational Theory and Mathematics, Pathology and Forensic Medicine and Oncology, having authored 13 papers that have together received 1.6k indexed citations. Recurring topics across this work include Genomics and Chromatin Dynamics (5 papers), RNA Research and Splicing (3 papers), Cancer Mechanisms and Therapy (2 papers), Computational Drug Discovery Methods (2 papers), CRISPR and Genetic Engineering (2 papers), Cancer Genomics and Diagnostics (2 papers), Acute Myeloid Leukemia Research (1 paper) and Genomics and Rare Diseases (1 paper). The work is most often cited by research in Cancer Research (312 citations), Molecular Biology (1.3k citations), Hematology (148 citations), Immunology (204 citations) and Oncology (238 citations). Peyton Greenside has collaborated with scholars based in United States, Germany and Austria. Frequent co-authors include Anshul Kundaje, M Snyder, Jonathan K. Pritchard, William J. Greenleaf, M. Ryan Corces, Ravindra Majeti, Jason D. Buenrostro, Julie L. Koenig, Steven M. Chan and Howard Y. Chang. Their work appears in journals such as Cancer Discovery, Nature Genetics, PLoS Biology, Nature Medicine and CPT Pharmacometrics & Systems Pharmacology.
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.