Eric T. Wang
- Molecular Biology top 0.5%
- RNA Research and Splicing 38
- RNA and protein synthesis mechanisms 17
- RNA modifications and cancer 17
- Muscle Physiology and Disorders 14
- Mitochondrial Function and Pathology 13
- CRISPR and Genetic Engineering 8
- RNA regulation and disease 7
- Cancer Research top 1%
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- Genetic Neurodegenerative Diseases 35
- Aging top 2%
- Genetics top 2%
- Co-authors
- Christopher B. BurgeRickard SandbergGary P. SchrothShujun LuoIrina KhrebtukovaStephen F. KingsmoreLu ZhangChristine Mayr
- Journals
- Proceedings of the National Academy of Sciences (7 papers)Cell Reports (4 papers)Nucleic Acids Research (4 papers)
- Partner nations
- United StatesJapanNetherlands
In The Last Decade
Eric T. Wang
85 papers receiving 9.4k citations
Hit Papers
Peers
Comparison fields: 5 of 171
- Molecular Biology 7.5k
- Cancer Research 1.4k
- Cellular and Molecular Neuroscience 1.2k
- Aging 98
- Genetics 1.1k
Countries citing papers authored by Eric T. Wang
This map shows the geographic impact of Eric T. Wang'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 Eric T. Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric T. Wang more than expected).
Fields of papers citing papers by Eric T. Wang
This network shows the impact of papers produced by Eric T. Wang. 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 Eric T. Wang. The network helps show where Eric T. Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Eric T. Wang, 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 | 2024 | 3 | |
| 2 | 2024 | 4 | |
| 3 | 2023 | 23 | |
| 4 | 2023 | 2 | |
| 5 | 2022 | 7 | |
| 6 | 2022 | 13 | |
| 7 | 2021 | 50 | |
| 8 | 2020 | 10 | |
| 9 | 2020 | 7 | |
| 10 | 2020 | 15 | |
| 11 | 2019 | 91 | |
| 12 | 2016 | 27 | |
| 13 | 2016 | 77 | |
| 14 | 2016 | 73 | |
| 15 | 2015 | 127 | |
| 16 | 2015 | 150 | |
| 17 | 2012 | 208 | |
| 18 | 2010 | 59 | |
| 19 | 2009 | 99 | |
| 20 | Alternative isoform regulation in human tissue transcriptomesbreakdown → | 2008 | 3796 |
About Eric T. Wang
Eric T. Wang is a scholar working on Cellular and Molecular Neuroscience, Aging, Molecular Biology, Neurology and Genetics, having authored 88 papers that have together received 9.6k indexed citations. Recurring topics across this work include RNA Research and Splicing (38 papers), Genetic Neurodegenerative Diseases (35 papers), RNA and protein synthesis mechanisms (17 papers), RNA modifications and cancer (17 papers), Muscle Physiology and Disorders (14 papers), Mitochondrial Function and Pathology (13 papers), CRISPR and Genetic Engineering (8 papers) and RNA regulation and disease (7 papers). The work is most often cited by research in Molecular Biology (7.5k citations), Cancer Research (1.4k citations), Cellular and Molecular Neuroscience (1.2k citations), Aging (98 citations) and Genetics (1.1k citations). Eric T. Wang has collaborated with scholars based in United States, Japan and Netherlands. Frequent co-authors include Christopher B. Burge, Rickard Sandberg, Gary P. Schroth, Shujun Luo, Irina Khrebtukova, Stephen F. Kingsmore, Lu Zhang, Christine Mayr, Daniel Ramsköld and Robert K. Moyzis. Their work appears in journals such as Proceedings of the National Academy of Sciences, Cell Reports, Nucleic Acids Research, Molecular Cell and Human Molecular Genetics.
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.