Nadia K. Litterman
- Molecular Biology
- Physiology top 10%
- Neurology top 5%
- Developmental Neuroscience top 5%
- Cellular and Molecular Neuroscience top 10%
- Co-authors
- Lee L. RubinAmy J. WagersLida KatsimpardiChristine MillerJohn W. ChenFrancesco S. LoffredoGregory R. WojtkiewiczRichard Lee
- Topics
- Computational Drug Discovery Methods (7 papers)Bioinformatics and Genomic Networks (4 papers)Pharmacogenetics and Drug Metabolism (3 papers)
- Partner nations
- United StatesAustraliaUnited Kingdom
In The Last Decade
Nadia K. Litterman
20 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 128
- Molecular Biology 624
- Physiology 285
- Neurology 177
- Developmental Neuroscience 174
- Cellular and Molecular Neuroscience 152
Countries citing papers authored by Nadia K. Litterman
This map shows the geographic impact of Nadia K. Litterman'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 Nadia K. Litterman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nadia K. Litterman more than expected).
Fields of papers citing papers by Nadia K. Litterman
This network shows the impact of papers produced by Nadia K. Litterman. 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 Nadia K. Litterman. The network helps show where Nadia K. Litterman may publish in the future.
Co-authorship network of co-authors of Nadia K. Litterman
This figure shows the co-authorship network connecting the top 25 collaborators of Nadia K. Litterman. A scholar is included among the top collaborators of Nadia K. Litterman based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nadia K. Litterman. Nadia K. Litterman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 9 | |
| 3 | 100 | |
| 4 | 34 | |
| 5 | 3 | |
| 6 | 10 | |
| 7 | 7 | |
| 8 | 30 | |
| 9 | 19 | |
| 10 | Vascular and Neurogenic Rejuvenation of the Aging Mouse Brain by Young Systemic Factorsbreakdown → | 760 |
| 11 | 5 | |
| 12 | 19 | |
| 13 | 22 | |
| 14 | 16 | |
| 15 | 14 | |
| 16 | 17 | |
| 17 | 75 | |
| 18 | 72 | |
| 19 | 28 | |
| 20 | 32 |
About Nadia K. Litterman
Nadia K. Litterman is a scholar working on Computational Theory and Mathematics, Pharmacology and Neurology, having authored 20 papers that have together received 1.3k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (7 papers), Bioinformatics and Genomic Networks (4 papers) and Pharmacogenetics and Drug Metabolism (3 papers). The work is most often cited by research in Aging (95 citations), Developmental Neuroscience (174 citations) and Neurology (177 citations). Nadia K. Litterman has collaborated with scholars based in United States, Australia and United Kingdom. Frequent co-authors include Lee L. Rubin, Amy J. Wagers, Lida Katsimpardi, Christine Miller, John W. Chen, Francesco S. Loffredo, Gregory R. Wojtkiewicz, Richard Lee, Sean Ekins and Azad Bonni. Their work appears in journals such as Science, Journal of the American Chemical Society and Journal of Neuroscience.
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.