Femina Kanji

10 papers and 591 indexed citations i.

About

Femina Kanji is a scholar working on Cancer Research, Genetics and Molecular Biology. According to data from OpenAlex, Femina Kanji has authored 10 papers receiving a total of 591 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Cancer Research, 4 papers in Genetics and 3 papers in Molecular Biology. Recurrent topics in Femina Kanji’s work include Breast Cancer Treatment Studies (3 papers), Advanced Breast Cancer Therapies (3 papers) and Virus-based gene therapy research (3 papers). Femina Kanji is often cited by papers focused on Breast Cancer Treatment Studies (3 papers), Advanced Breast Cancer Therapies (3 papers) and Virus-based gene therapy research (3 papers). Femina Kanji collaborates with scholars based in Canada, United States and South Korea. Femina Kanji's co-authors include David H. Kirn, John C. Bell, Tae-Ho Hwang, Susan Dent, Chris W. Brown, Steve H. Thorne, Jean‐Simon Diallo, Rebecca C. Auer, Fabrice Le Bœuf and Theresa Falls and has published in prestigious journals such as Journal of Clinical Investigation, Journal of Clinical Oncology and Molecular Therapy.

In The Last Decade

Co-authorship network of co-authors of Femina Kanji i

Fields of papers citing papers by Femina Kanji

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Femina Kanji

Since Specialization
Citations

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

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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2025