Beijing Wu
- Aging top 2%
- Genetics, Aging, and Longevity in Model Organisms 2
- Molecular Biology top 1%
- Single-cell and spatial transcriptomics 6
- Genomics and Chromatin Dynamics 3
- CRISPR and Genetic Engineering 2
- Gene Regulatory Network Analysis 2
- Epigenetics and DNA Methylation 2
- Cancer Research top 2%
- Cancer Genomics and Diagnostics 3
- Biophysics top 1%
- Immunology top 2%
- T-cell and B-cell Immunology 2
- Co-authors
- William J. GreenleafJason D. BuenrostroHoward Y. ChangM SnyderAlicia N. SchepUlrike LitzenburgerMichael L. GonzalesM. Ryan Corces
- Partner nations
- United StatesSwedenCanada
In The Last Decade
Beijing Wu
10 papers receiving 5.8k citations
Hit Papers
Peers
Comparison fields: 5 of 126
- Aging 135
- Molecular Biology 4.8k
- Cancer Research 1.0k
- Biophysics 303
- Immunology 1.1k
Countries citing papers authored by Beijing Wu
This map shows the geographic impact of Beijing Wu'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 Beijing Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Beijing Wu more than expected).
Fields of papers citing papers by Beijing Wu
This network shows the impact of papers produced by Beijing Wu. 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 Beijing Wu. The network helps show where Beijing Wu may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Beijing Wu, 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 | 2019 | 152 | |
| 2 | Integrated Single-Cell Analysis Maps the Continuous Regulatory Landscape of Human Hematopoietic Differentiationbreakdown → | 2018 | 397 |
| 3 | 2018 | 213 | |
| 4 | chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic databreakdown → | 2017 | 770 |
| 5 | 2017 | 88 | |
| 6 | Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolutionbreakdown → | 2016 | 656 |
| 7 | Single-cell chromatin accessibility reveals principles of regulatory variationbreakdown → | 2015 | 1460 |
| 8 | ATAC‐seq: A Method for Assaying Chromatin Accessibility Genome‐Widebreakdown → | 2015 | 1978 |
| 9 | 2014 | 82 | |
| 10 | 2013 | 36 |
About Beijing Wu
Beijing Wu is a scholar working on Aging, Cancer Research and Endocrine and Autonomic Systems, having authored 10 papers that have together received 5.8k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (6 papers), Cancer Genomics and Diagnostics (3 papers), Genomics and Chromatin Dynamics (3 papers), CRISPR and Genetic Engineering (2 papers), Gene Regulatory Network Analysis (2 papers), Genetics, Aging, and Longevity in Model Organisms (2 papers), Epigenetics and DNA Methylation (2 papers) and T-cell and B-cell Immunology (2 papers). The work is most often cited by research in Aging (135 citations), Molecular Biology (4.8k citations) and Cancer Research (1.0k citations). Beijing Wu has collaborated with scholars based in United States, Sweden and Canada. Frequent co-authors include William J. Greenleaf, Jason D. Buenrostro, Howard Y. Chang, M Snyder, Alicia N. Schep, Ulrike Litzenburger, Michael L. Gonzales, M. Ryan Corces, Ravindra Majeti and Jonathan K. Pritchard. Their work appears in journals such as Nature, Nature Genetics, Cell, Nature Methods and Genome biology.
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.