Brian Befano

43 papers receiving 1.6k citations

Peers

Brian Befano
Comparison fields: 5 of 99
  • Epidemiology 1.3k
  • Health Informatics 47
  • Microbiology 176
  • Obstetrics and Gynecology 185
  • Oncology 427
Replace Maria Demarco with:
Maria Demarco United States
Sanjay Gupta India
Nancy Poitras United States
Michelle J. Khan United States
Didem Egemen United States
Dirk van Niekerk Canada
Marta del Pino Spain
Adela Saco Spain
Martial Guillaud Canada
Pamela Michelow South Africa
Brian Befano relative to Maria Demarco United States Maria Demarco's profile →
Citations per field
00.5×10×20×26.5×
Maria Demarco · 1×
Citations per year

Countries citing papers authored by Brian Befano

Since Specialization
Citations

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

Fields of papers citing papers by Brian Befano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018258
2 2010193
3 2020129
4 2017111
5 201478
6 201072
7 201871
8 201663
9 201748
10 202048
11 201546
12 202144
13 202044
14 201743
15 202241
16 201540
17 202131
18 201531
19 202325
20 201921

About Brian Befano

Brian Befano is a scholar working on Epidemiology, Oncology, Artificial Intelligence, Surgery and Molecular Biology, having authored 44 papers that have together received 1.7k indexed citations. Recurring topics across this work include Cervical Cancer and HPV Research (41 papers), AI in cancer detection (15 papers), Global Cancer Incidence and Screening (12 papers), Genital Health and Disease (12 papers), Endometrial and Cervical Cancer Treatments (6 papers), Molecular Biology Techniques and Applications (6 papers), Hepatitis B Virus Studies (5 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). The work is most often cited by research in Epidemiology (1.3k citations), Health Informatics (47 citations), Microbiology (176 citations), Obstetrics and Gynecology (185 citations) and Oncology (427 citations). Brian Befano has collaborated with scholars based in United States, Spain and Costa Rica. Frequent co-authors include Mark Schiffman, Nicolas Wentzensen, Julia C. Gage, Philip E. Castle, Ana Cecilia Rodríguez, Li C. Cheung, Thomas Lorey, Hormuzd A. Katki, Nancy Poitras and Maria Demarco. Their work appears in journals such as International Journal of Cancer, JNCI Journal of the National Cancer Institute, Gynecologic Oncology, Journal of Lower Genital Tract Disease and Journal of Clinical Microbiology.

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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