Maya Madhavan

530 citations
20 papers · 219 · h-index 8

Impact in

Papers in

    • vaccines and immunoinformatics approaches 3
    • RNA Interference and Gene Delivery 2
    • Angiogenesis and VEGF in Cancer 2
    • SARS-CoV-2 and COVID-19 Research 2
    • Viral Infections and Outbreaks Research 2

Maya Madhavan

19 papers receiving 211 citations

Peers

Maya Madhavan
Comparison fields: 5 of 62
  • Immunology and Allergy 24
  • Cancer Research 42
  • Biomaterials 24
  • Oncology 44
  • Cell Biology 22
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Maya Madhavan relative to Lukas M. Altenburger Austria Lukas M. Altenburger's profile →
Citations per field
00.5×1.5×1.8×
Lukas M. Altenburger · 1×
Citations per year

Countries citing papers authored by Maya Madhavan

Since Specialization
Citations

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

Fields of papers citing papers by Maya Madhavan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 202241
2 200141
3 201335
4 200221
5 202219
6 20008
7 20187
8 20227
9 20227
10
Apoptotic index: use in predicting recurrence in breast cancer patients.
20026
11 20235
12 20215
13 20165
14 20203
15 20223
16 20222
17 20202
18
Endometrial mast cells in loop menorrhagia.
19741
19
Nm23H1 and p53 proteins are differentially correlated to metastasis in breast carcinoma.
20021
20 20230

About Maya Madhavan

Maya Madhavan is a scholar working on Molecular Biology, Infectious Diseases, Oncology, Health and Pulmonary and Respiratory Medicine, having authored 20 papers that have together received 219 indexed citations. Recurring topics across this work include vaccines and immunoinformatics approaches (3 papers), RNA Interference and Gene Delivery (2 papers), Cancer Cells and Metastasis (2 papers), Monoclonal and Polyclonal Antibodies Research (2 papers), SARS-CoV-2 and COVID-19 Research (2 papers), Angiogenesis and VEGF in Cancer (2 papers), Vaccine Coverage and Hesitancy (2 papers) and Viral Infections and Outbreaks Research (2 papers). The work is most often cited by research in Immunology and Allergy (24 citations), Cancer Research (42 citations), Biomaterials (24 citations), Oncology (44 citations) and Cell Biology (22 citations). Maya Madhavan has collaborated with scholars based in India, United States and Saudi Arabia. Frequent co-authors include Prabha Balaram, Priya Srinivas, Elizabeth Abraham, Aleyamma Mathew, Iqbal Ike K. Ahmed, Vinoy Thomas, James M. Donahue, Seeram Ramakrishna, Mina Zare and Iqbal Ahmed. Their work appears in journals such as Modern Pathology, Pharmaceutics, International Journal of Health Services, Theoretical Computer Science and Neurology.

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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