May Khanna

3.1k citations
56 papers · 2.0k · h-index 31

Impact in

Papers in

    • RNA Research and Splicing 9
    • RNA and protein synthesis mechanisms 5
    • Ion channel regulation and function 4
    • Axon Guidance and Neuronal Signaling 10
    • Neuroscience and Neuropharmacology Research 6
    • Neuropeptides and Animal Physiology 4

May Khanna

54 papers receiving 2.0k citations

Peers

May Khanna
Comparison fields: 5 of 101
  • Cellular and Molecular Neuroscience 512
  • Physiology 466
  • Molecular Biology 1.2k
  • Neurology 228
  • Cell Biology 182
Replace Graziella Cappelletti with:
Graziella Cappelletti Italy
Xiaojiang Li China
Yichin Liu United States
Chikara Murakata Japan
Gail M. Seigel United States
Hyangshuk Rhim South Korea
Jung Jin Hwang South Korea
Qihua He China
Wenbo Zhou United States
H. Bea Kuiperij Netherlands
May Khanna relative to Graziella Cappelletti Italy Graziella Cappelletti's profile →
Citations per field
00.5×1.5×2.0×
Graziella Cappelletti · 1×
Citations per year

Countries citing papers authored by May Khanna

Since Specialization
Citations

This map shows the geographic impact of May Khanna'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 May Khanna with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites May Khanna more than expected).

Fields of papers citing papers by May Khanna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by May Khanna. 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 May Khanna. The network helps show where May Khanna may publish in the future.

Co-authors

The 25 scholars most cited alongside May Khanna, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with May Khanna Line = papers co-authored together May Khanna links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 56 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008150
2 2014116
3 2019104
4 2016100
5 200394
6 201166
7 201765
8 201563
9 201957
10 201151
11 201850
12 201649
13 201547
14 201246
15 201846
16 202243
17 201142
18 201342
19 201541
20 201237

About May Khanna

May Khanna is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience, Physiology, Neurology and Cancer Research, having authored 56 papers that have together received 2.0k indexed citations. Recurring topics across this work include Pain Mechanisms and Treatments (11 papers), Axon Guidance and Neuronal Signaling (10 papers), RNA Research and Splicing (9 papers), Amyotrophic Lateral Sclerosis Research (7 papers), Neuroscience and Neuropharmacology Research (6 papers), RNA and protein synthesis mechanisms (5 papers), Ion channel regulation and function (4 papers) and Neuropeptides and Animal Physiology (4 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (512 citations), Physiology (466 citations), Molecular Biology (1.2k citations), Neurology (228 citations) and Cell Biology (182 citations). May Khanna has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Rajesh Khanna, Aubin Moutal, Liberty François‐Moutal, Samantha Perez‐Miller, David D. Scott, Samy O. Meroueh, Xiaofang Yang, Erik T. Dustrude, Yuying Wang and Stefan Kaskel. Their work appears in journals such as Pain, Channels, Proceedings of the National Academy of Sciences, ACS Chemical Biology and RNA.

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.

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