Vidya Niranjan

84 papers receiving 721 citations

Peers

Vidya Niranjan
Comparison fields: 5 of 103
  • Molecular Medicine 89
  • Computational Theory and Mathematics 178
  • Toxicology 21
  • Endocrinology 30
  • Organic Chemistry 143
Replace Mohd Danishuddin with:
Mohd Danishuddin India
Devadasan Velmurugan India
Yeh Chen Taiwan
Elijah Kolawole Oladipo Nigeria
R J Polzer United States
Thales Kronenberger Germany
Nagakumar Bharatham South Korea
Wesam H. Abdulaal Saudi Arabia
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Citations per field
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Citations per year

Countries citing papers authored by Vidya Niranjan

Since Specialization
Citations

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

Fields of papers citing papers by Vidya Niranjan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 94 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Antimicrobial resistance pattern in Escherichia coli causing urinary tract infection among inpatients.
201480
2 201365
3 202039
4 202328
5 202125
6 202225
7 202123
8 200321
9 202221
10 202019
11 202117
12 202017
13 202317
14 202216
15 202216
16 201915
17 201115
18 201813
19 202113
20 201312

About Vidya Niranjan

Vidya Niranjan is a scholar working on Molecular Biology, Computational Theory and Mathematics, Organic Chemistry, Plant Science and Infectious Diseases, having authored 94 papers that have together received 776 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (27 papers), Synthesis and biological activity (17 papers), Genomics and Phylogenetic Studies (6 papers), Cancer therapeutics and mechanisms (5 papers), Protein Structure and Dynamics (5 papers), Cancer Genomics and Diagnostics (5 papers), SARS-CoV-2 and COVID-19 Research (5 papers) and Bioactive Compounds and Antitumor Agents (4 papers). The work is most often cited by research in Molecular Medicine (89 citations), Computational Theory and Mathematics (178 citations), Toxicology (21 citations), Endocrinology (30 citations) and Organic Chemistry (143 citations). Vidya Niranjan has collaborated with scholars based in India, Algeria and United States. Frequent co-authors include Akshay Uttarkar, A Malini, Sinosh Skariyachan, Dharshini Gopal, Raviraj Kusanur, Jitendra Kumar, Shweta Chakrabarti, Vasanthan Jayakumar, Raja C. Mugasimangalam and Saraswathi Vishveshwara. Their work appears in journals such as Journal of Biomolecular Structure and Dynamics, Infection Genetics and Evolution, Molecular Simulation, Computers in Biology and Medicine and International Journal of Biological Macromolecules.

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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