Ha Vu
Impact in
- Aging top 10%
- Genetics, Aging, and Longevity in Model Organisms
- Endocrine and Autonomic Systems top 10%
- Circadian rhythm and melatonin
Papers in
-
- Genomics and Chromatin Dynamics 5
- Epigenetics and DNA Methylation 3
- Machine Learning in Bioinformatics 2
- RNA modifications and cancer 2
- RNA Research and Splicing 2
-
- Birth, Development, and Health 2
- Co-authors
- Jason Ernst (7 shared papers)Soo Bin Kwon (3 shared papers)Ahmet Ay (3 shared papers)Krista K. Ingram (1 shared paper)Allan Filipowicz (1 shared paper)Charles E. Breeze (1 shared paper)Ake T. Lu (1 shared paper)Michael J. Thompson (1 shared paper)
- Journals
- Genome biology (2 papers)iScience (1 paper)Scientific Reports (1 paper)Nature Communications (1 paper)Development (1 paper)
- Partner nations
- United StatesBrazilChile
In The Last Decade
Ha Vu
14 papers receiving 320 citations
Peers
Comparison fields: 5 of 55
- Aging 26
- Endocrine and Autonomic Systems 55
- Molecular Biology 225
- Obstetrics and Gynecology 24
- Experimental and Cognitive Psychology 38
Countries citing papers authored by Ha Vu
This map shows the geographic impact of Ha Vu'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 Ha Vu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ha Vu more than expected).
Fields of papers citing papers by Ha Vu
This network shows the impact of papers produced by Ha Vu. 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 Ha Vu. The network helps show where Ha Vu may publish in the future.
Co-authors
The 25 scholars most cited alongside Ha Vu, 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 | 2022 | 104 | |
| 2 | 2017 | 58 | |
| 3 | 2022 | 55 | |
| 4 | 2018 | 32 | |
| 5 | 2022 | 20 | |
| 6 | 2022 | 19 | |
| 7 | 2024 | 13 | |
| 8 | 2023 | 9 | |
| 9 | 2019 | 7 | |
| 10 | 2022 | 2 | |
| 11 | 2023 | 2 | |
| 12 | 2019 | 2 | |
| 13 | 2023 | 1 | |
| 14 | 2021 | 1 |
About Ha Vu
Ha Vu is a scholar working on Molecular Biology, Pediatrics, Perinatology and Child Health, Obstetrics and Gynecology, Immunology and Infectious Diseases, having authored 14 papers that have together received 325 indexed citations. Recurring topics across this work include Genomics and Chromatin Dynamics (5 papers), Epigenetics and DNA Methylation (3 papers), Pregnancy and preeclampsia studies (3 papers), Machine Learning in Bioinformatics (2 papers), Reproductive System and Pregnancy (2 papers), RNA modifications and cancer (2 papers), Birth, Development, and Health (2 papers) and RNA Research and Splicing (2 papers). The work is most often cited by research in Aging (26 citations), Endocrine and Autonomic Systems (55 citations), Molecular Biology (225 citations), Obstetrics and Gynecology (24 citations) and Experimental and Cognitive Psychology (38 citations). Ha Vu has collaborated with scholars based in United States, Brazil and Chile. Frequent co-authors include Jason Ernst, Soo Bin Kwon, Ahmet Ay, Krista K. Ingram, Allan Filipowicz, Charles E. Breeze, Ake T. Lu, Michael J. Thompson, Kasper D. Hansen and Wanding Zhou. Their work appears in journals such as Genome biology, iScience, Scientific Reports, Nature Communications and Development.
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.