Peter V. Kharchenko
- Molecular Biology top 0.5%
- Single-cell and spatial transcriptomics 26
- Genomics and Chromatin Dynamics 21
- RNA Research and Splicing 11
- Epigenetics and DNA Methylation 10
- RNA modifications and cancer 10
- Genomics and Phylogenetic Studies 7
- Cancer Research top 0.5%
- Cancer Genomics and Diagnostics 9
- Biophysics top 0.5%
- Sensory Systems top 1%
- Immunology top 2%
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- Chromosomal and Genetic Variations 10
- Co-authors
- Peter J. ParkDavid T. ScaddenLev SilbersteinMichael TolstorukovMitzi I. KurodaArtyom A. AlekseyenkoSten LinnarssonDmitry Usoskin
- Journals
- Blood (8 papers)Proceedings of the National Academy of Sciences (8 papers)Nature Methods (7 papers)
- Partner nations
- United StatesSwedenRussia
In The Last Decade
Peter V. Kharchenko
90 papers receiving 11.2k citations
Hit Papers
Peers
Comparison fields: 5 of 157
- Molecular Biology 8.5k
- Cancer Research 1.8k
- Biophysics 477
- Sensory Systems 309
- Immunology 1.1k
Countries citing papers authored by Peter V. Kharchenko
This map shows the geographic impact of Peter V. Kharchenko'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 Peter V. Kharchenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter V. Kharchenko more than expected).
Fields of papers citing papers by Peter V. Kharchenko
This network shows the impact of papers produced by Peter V. Kharchenko. 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 Peter V. Kharchenko. The network helps show where Peter V. Kharchenko may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Peter V. Kharchenko, 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 | 2 | |
| 2 | 2024 | 7 | |
| 3 | 2024 | 8 | |
| 4 | 2023 | 5 | |
| 5 | 2023 | 11 | |
| 6 | 2022 | 23 | |
| 7 | 2022 | 62 | |
| 8 | 2022 | 38 | |
| 9 | 2021 | 148 | |
| 10 | 2021 | 183 | |
| 11 | How to perform quantitative single cell proteomics with SCoPE2 | 2020 | 1 |
| 12 | 2020 | 115 | |
| 13 | 2019 | 5 | |
| 14 | 2017 | 222 | |
| 15 | 2017 | 88 | |
| 16 | 2011 | 336 | |
| 17 | 2011 | 146 | |
| 18 | 2010 | 141 | |
| 19 | 2009 | 79 | |
| 20 | 2008 | 30 |
About Peter V. Kharchenko
Peter V. Kharchenko is a scholar working on Computational Mathematics, Biophysics, Molecular Biology, Immunology and Cancer Research, having authored 94 papers that have together received 11.2k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (26 papers), Genomics and Chromatin Dynamics (21 papers), RNA Research and Splicing (11 papers), Epigenetics and DNA Methylation (10 papers), RNA modifications and cancer (10 papers), Chromosomal and Genetic Variations (10 papers), Cancer Genomics and Diagnostics (9 papers) and Genomics and Phylogenetic Studies (7 papers). The work is most often cited by research in Molecular Biology (8.5k citations), Cancer Research (1.8k citations), Biophysics (477 citations), Sensory Systems (309 citations) and Immunology (1.1k citations). Peter V. Kharchenko has collaborated with scholars based in United States, Sweden and Russia. Frequent co-authors include Peter J. Park, David T. Scadden, Lev Silberstein, Michael Tolstorukov, Mitzi I. Kuroda, Artyom A. Alekseyenko, Sten Linnarsson, Dmitry Usoskin, Patrik Ernfors and Hind Abdo. Their work appears in journals such as Blood, Proceedings of the National Academy of Sciences, Nature Methods, Nature Biotechnology 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.