Deeksha Salaria

683 citations
22 papers · 441 · h-index 12

Impact in

Papers in

Deeksha Salaria

19 papers receiving 430 citations

Peers

Deeksha Salaria
Comparison fields: 5 of 76
  • Pharmacology 98
  • Complementary and alternative medicine 87
  • Biochemistry 50
  • Food Science 144
  • Computational Theory and Mathematics 123
Replace Sefren Geiner Tumilaar with:
Sefren Geiner Tumilaar Indonesia
Shahenur Alam Sakib Bangladesh
Rajan Rolta India
Akhtar Muhammad Pakistan
Jae Hyoung Song South Korea
Sushma Pradeep India
Akinwunmi Oluwaseun Adeoye Nigeria
Nurdjannah Jane Niode Indonesia
Abayomi Emmanuel Adegboyega Nigeria
Jihan M. Badr Egypt
Deeksha Salaria relative to Sefren Geiner Tumilaar Indonesia Sefren Geiner Tumilaar's profile →
Citations per field
00.5×1.6×
Sefren Geiner Tumilaar · 1×
Citations per year

Countries citing papers authored by Deeksha Salaria

Since Specialization
Citations

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

Fields of papers citing papers by Deeksha Salaria

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Deeksha Salaria, 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 Deeksha Salaria Line = papers co-authored together Deeksha Salaria links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202092
2 202170
3 202133
4 202230
5 202029
6 202226
7 202126
8 202124
9 202221
10 202218
11 202317
12 202214
13 20239
14 20237
15 20236
16 20245
17 20245
18 20235
19 20224
20 20250

About Deeksha Salaria

Deeksha Salaria is a scholar working on Plant Science, Computational Theory and Mathematics, Pharmacology, Food Science and Molecular Biology, having authored 22 papers that have together received 441 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (9 papers), Essential Oils and Antimicrobial Activity (9 papers), Pharmacological Effects of Natural Compounds (7 papers), Ethnobotanical and Medicinal Plants Studies (5 papers), SARS-CoV-2 and COVID-19 Research (4 papers), Phytochemistry and Biological Activities (3 papers), Medicinal Plants and Neuroprotection (3 papers) and Natural product bioactivities and synthesis (2 papers). The work is most often cited by research in Pharmacology (98 citations), Complementary and alternative medicine (87 citations), Biochemistry (50 citations), Food Science (144 citations) and Computational Theory and Mathematics (123 citations). Deeksha Salaria has collaborated with scholars based in India, Nigeria and South Korea. Frequent co-authors include Rajan Rolta, Kamal Dev, Anuradha Sourirajan, David J. Baumler, Vikas Kumar, Chirag Patel, Olatomide A. Fadare, Rohitash Yadav, Mohammed Imran and Bhanu Sharma. Their work appears in journals such as Journal of Biomolecular Structure and Dynamics, Molecules, Applied Sciences, Biomedicines and Biochemical and Biophysical Research Communications.

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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