Arun K. Shukla
- Cellular and Molecular Neuroscience top 0.5%
- Neuropeptides and Animal Physiology 43
- Molecular Biology top 1%
- Receptor Mechanisms and Signaling 75
- Protein Kinase Regulation and GTPase Signaling 9
- Chemical Synthesis and Analysis 6
- Spectroscopy top 1%
- Mass Spectrometry Techniques and Applications 10
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- Monoclonal and Polyclonal Antibodies Research 25
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- Complement system in diseases 8
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- Pharmacological Effects and Assays 6
- Co-authors
- Robert J. LefkowitzKunhong XiaoSeungkirl AhnSudha K. ShenoyEshan GhoshÉric ReiterR.J. LefkowitzPunita Kumari
- Partner nations
- IndiaUnited StatesGermany
In The Last Decade
Arun K. Shukla
95 papers receiving 6.1k citations
Hit Papers
Peers
Comparison fields: 5 of 114
- Cellular and Molecular Neuroscience 3.1k
- Molecular Biology 5.3k
- Computational Theory and Mathematics 601
- Spectroscopy 619
- Radiology, Nuclear Medicine and Imaging 738
Countries citing papers authored by Arun K. Shukla
This map shows the geographic impact of Arun K. Shukla'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 Arun K. Shukla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arun K. Shukla more than expected).
Fields of papers citing papers by Arun K. Shukla
This network shows the impact of papers produced by Arun K. Shukla. 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 Arun K. Shukla. The network helps show where Arun K. Shukla may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Arun K. Shukla, 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 | 0 | |
| 2 | 2024 | 15 | |
| 3 | 2024 | 2 | |
| 4 | 2023 | 34 | |
| 5 | 2023 | 19 | |
| 6 | 2022 | 121 | |
| 7 | 2022 | 17 | |
| 8 | 2022 | 6 | |
| 9 | 2021 | 3 | |
| 10 | 2021 | 3 | |
| 11 | 2020 | 22 | |
| 12 | 2019 | 29 | |
| 13 | 2018 | 75 | |
| 14 | Distinct Phosphorylation Sites on the β 2 -Adrenergic Receptor Establish a Barcode That Encodes Differential Functions of β-Arrestinbreakdown → | 2011 | 497 |
| 15 | 2010 | 123 | |
| 16 | 2010 | 164 | |
| 17 | 2009 | 125 | |
| 18 | 2009 | 193 | |
| 19 | Functional specialization of β-arrestin interactions revealed by proteomic analysisbreakdown → | 2007 | 327 |
| 20 | Genetic Resources of Aonla (Emblica of ficinalis Gaertn.) | 2005 | 2 |
About Arun K. Shukla
Arun K. Shukla is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Radiology, Nuclear Medicine and Imaging, having authored 101 papers that have together received 6.1k indexed citations. Recurring topics across this work include Receptor Mechanisms and Signaling (75 papers), Neuropeptides and Animal Physiology (43 papers), Monoclonal and Polyclonal Antibodies Research (25 papers), Mass Spectrometry Techniques and Applications (10 papers), Protein Kinase Regulation and GTPase Signaling (9 papers), Complement system in diseases (8 papers), Pharmacological Effects and Assays (6 papers) and Chemical Synthesis and Analysis (6 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (3.1k citations), Molecular Biology (5.3k citations) and Computational Theory and Mathematics (601 citations). Arun K. Shukla has collaborated with scholars based in India, United States and Germany. Frequent co-authors include Robert J. Lefkowitz, Kunhong Xiao, Seungkirl Ahn, Sudha K. Shenoy, Eshan Ghosh, Éric Reiter, R.J. Lefkowitz, Punita Kumari, Sudarshan Rajagopal and Shubhi Pandey. Their work appears in journals such as Nature, Science and Cell.
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.