Sibi Raj

13 papers receiving 637 citations

Sibi Raj's Hit Papers

Specific targeting cancer cells with nanoparticles and drug delivery in cancer therapy 2019 · 340 citations
3400+2+4Years since publication100200300

Peers

Sibi Raj
Comparison fields: 5 of 82
  • Biomaterials 167
  • Molecular Medicine 25
  • Biomedical Engineering 182
  • Infectious Diseases 69
  • Computational Theory and Mathematics 63
Replace Sai Kiran S. S. Pindiprolu with:
Sai Kiran S. S. Pindiprolu India
Parastoo Tarighi Iran
Mojtaba Falahati Iran
Huy Ngo United States
Renata Salgado Fernandes Brazil
Bárbara Carreira Portugal
Longbing Ling China
Liqiang Chen United States
Joachim Caron France
Sibi Raj relative to Sai Kiran S. S. Pindiprolu India Sai Kiran S. S. Pindiprolu's profile →
Citations per field
00.5×1.6×
Sai Kiran S. S. Pindiprolu · 1×
Citations per year

Countries citing papers authored by Sibi Raj

Since Specialization
Citations

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

Fields of papers citing papers by Sibi Raj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

15 of 15 papers shown
#Work
1
Specific targeting cancer cells with nanoparticles and drug delivery in cancer therapy
Hit paper breakdown →
2019340
2 2022117
3
In silico identification of potent FDA approved drugs against Coronavirus COVID-19 main protease: A drug repurposing approach
202055
4 202037
5 202027
6 202117
7 202012
8 202112
9 202011
10 20209
11 20225
12 20232
13 20231
14 20211
15 20230

About Sibi Raj

Sibi Raj is a scholar working on Molecular Biology, Infectious Diseases, Pulmonary and Respiratory Medicine, Computational Theory and Mathematics and Biomedical Engineering, having authored 15 papers that have together received 646 indexed citations. Recurring topics across this work include SARS-CoV-2 and COVID-19 Research (4 papers), Metabolism, Diabetes, and Cancer (2 papers), Computational Drug Discovery Methods (2 papers), COVID-19 Clinical Research Studies (2 papers), Genetics and Neurodevelopmental Disorders (1 paper), Pluripotent Stem Cells Research (1 paper), Liver physiology and pathology (1 paper) and Inhalation and Respiratory Drug Delivery (1 paper). The work is most often cited by research in Biomaterials (167 citations), Molecular Medicine (25 citations), Biomedical Engineering (182 citations), Infectious Diseases (69 citations) and Computational Theory and Mathematics (63 citations). Sibi Raj has collaborated with scholars based in India, United States and United Arab Emirates. Frequent co-authors include Dhruv Kumar, Kavindra Kumar Kesari, Ramesh Choudhari, Janne Ruokolainen, Mohammad Amjad Kamal, Bhudev C. Das, Neha Garg, Sartaj Khurana, Brijesh Rathi and Arun Kumar. Their work appears in journals such as Life Sciences, Nanomedicine, Seminars in Cancer Biology, BMC Pediatrics and Progress in molecular biology and translational science.

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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