Fabio Boniolo

428 citations
5 papers · 119 · h-index 4

Impact in

Papers in

    • Bioinformatics and Genomic Networks 2
    • Biomedical Text Mining and Ontologies 1
    • Cancer Genomics and Diagnostics 1
    • MicroRNA in disease regulation 1

Fabio Boniolo

5 papers receiving 118 citations

Peers

Fabio Boniolo
Comparison fields: 5 of 51
  • Health Informatics 13
  • Cancer Research 20
  • Computational Theory and Mathematics 21
  • Biophysics 7
  • Radiology, Nuclear Medicine and Imaging 16
Replace Anastasia Shneyderman with:
Anastasia Shneyderman Hong Kong
Ashwin V. Kammula United States
Irina Balaur Luxembourg
Huiping Shi China
Maximilian Alber Germany
Andreas Spitzmüller Germany
Sangwon Shin South Korea
Tongqi Qian United States
Iga Kołodziejczak Poland
Lea Seep Germany
Fabio Boniolo relative to Anastasia Shneyderman Hong Kong Anastasia Shneyderman's profile →
Citations per field
00.5×2.7×
Anastasia Shneyderman · 1×
Citations per year

Countries citing papers authored by Fabio Boniolo

Since Specialization
Citations

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

Fields of papers citing papers by Fabio Boniolo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Fabio Boniolo, 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 Fabio Boniolo Line = papers co-authored together Fabio Boniolo links everyone, so they are left out of the graph.

All Works

About Fabio Boniolo

Fabio Boniolo is a scholar working on Molecular Biology, Cancer Research, Surgery, Artificial Intelligence and Computational Theory and Mathematics, having authored 5 papers that have together received 119 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (2 papers), Biomedical Text Mining and Ontologies (1 paper), Dental Implant Techniques and Outcomes (1 paper), Computational Drug Discovery Methods (1 paper), Orthopaedic implants and arthroplasty (1 paper), Bone Tissue Engineering Materials (1 paper), Cancer Genomics and Diagnostics (1 paper) and MicroRNA in disease regulation (1 paper). The work is most often cited by research in Health Informatics (13 citations), Cancer Research (20 citations), Computational Theory and Mathematics (21 citations), Biophysics (7 citations) and Radiology, Nuclear Medicine and Imaging (16 citations). Fabio Boniolo has collaborated with scholars based in Germany, United States and Brazil. Frequent co-authors include Michael P. Menden, Dieter Saur, Benjamin Schubert, Salvatore Benfatto, Dominik Sturm, Volker Hovestadt, Marc Zapatka, David Capper, Martin Sill and Eilís Pérez. Their work appears in journals such as Bioinformatics, Frontiers in Cell and Developmental Biology, Philosophy & Technology and Expert Opinion on Drug Discovery.

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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