Uma Mudunuri
Impact in
- Molecular Biology top 10%
- RNA and protein synthesis mechanisms
- DNA and Nucleic Acid Chemistry
- Genomics and Chromatin Dynamics
- Advanced biosensing and bioanalysis techniques
- RNA modifications and cancer
- Genomics and Phylogenetic Studies
Papers in
-
- RNA and protein synthesis mechanisms 7
- Genomics and Chromatin Dynamics 5
- Genomics and Phylogenetic Studies 4
- Bioinformatics and Genomic Networks 4
- Gene expression and cancer classification 4
- Biomedical Text Mining and Ontologies 3
- CRISPR and Genetic Engineering 2
- Genetics 4
- Co-authors
- Regina Z. Cer (11 shared papers)Robert M. Stephens (15 shared papers)Ming Yi (9 shared papers)Frank J. Lebeda (5 shared papers)R. Scott Stephens (2 shared papers)Anney Che (5 shared papers)Brian T. Luke (12 shared papers)Jack Collins (10 shared papers)
- Journals
- Bioinformatics (4 papers)Nucleic Acids Research (4 papers)Genome biology (2 papers)Microscopy and Microanalysis (1 paper)PLoS Genetics (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Uma Mudunuri
23 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 112
- Molecular Biology 755
- Cancer Research 126
- Genetics 137
- Computational Theory and Mathematics 75
- Aging 7
Countries citing papers authored by Uma Mudunuri
This map shows the geographic impact of Uma Mudunuri'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 Uma Mudunuri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Uma Mudunuri more than expected).
Fields of papers citing papers by Uma Mudunuri
This network shows the impact of papers produced by Uma Mudunuri. 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 Uma Mudunuri. The network helps show where Uma Mudunuri may publish in the future.
Co-authors
The 25 scholars most cited alongside Uma Mudunuri, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2009 | 305 | |
| 2 | 2009 | 264 | |
| 3 | 2012 | 129 | |
| 4 | 2004 | 88 | |
| 5 | 2010 | 76 | |
| 6 | 2013 | 33 | |
| 7 | 2012 | 32 | |
| 8 | 2012 | 27 | |
| 9 | 2013 | 19 | |
| 10 | 2010 | 19 | |
| 11 | 2009 | 15 | |
| 12 | 2009 | 15 | |
| 13 | 2015 | 14 | |
| 14 | 2015 | 14 | |
| 15 | 2021 | 13 | |
| 16 | 2006 | 8 | |
| 17 | 2018 | 7 | |
| 18 | 2011 | 5 | |
| 19 | 2009 | 2 | |
| 20 | 2011 | 1 |
About Uma Mudunuri
Uma Mudunuri is a scholar working on Molecular Biology, Genetics, Neurology, Cancer Research and Oncology, having authored 24 papers that have together received 1.1k indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (7 papers), Genomics and Chromatin Dynamics (5 papers), Genomics and Phylogenetic Studies (4 papers), Bioinformatics and Genomic Networks (4 papers), Gene expression and cancer classification (4 papers), Botulinum Toxin and Related Neurological Disorders (3 papers), Biomedical Text Mining and Ontologies (3 papers) and CRISPR and Genetic Engineering (2 papers). The work is most often cited by research in Molecular Biology (755 citations), Cancer Research (126 citations), Genetics (137 citations), Computational Theory and Mathematics (75 citations) and Aging (7 citations). Uma Mudunuri has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Regina Z. Cer, Robert M. Stephens, Ming Yi, Frank J. Lebeda, R. Scott Stephens, Anney Che, Brian T. Luke, Jack Collins, Albino Bacolla and Natalia Volfovsky. Their work appears in journals such as Bioinformatics, Nucleic Acids Research, Genome biology, Microscopy and Microanalysis and PLoS 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.