Fabio De Bona

554 citations
14 papers · 358 indexed · h-index 9
Topics
Catalytic C–H Functionalization Methods (5 papers)RNA and protein synthesis mechanisms (4 papers)Genomics and Phylogenetic Studies (4 papers)
Partner nations
GermanyItalySwitzerland

In The Last Decade

Fabio De Bona

13 papers receiving 341 citations

Peers

Fabio De Bona
Comparison fields: 5 of 55
  • Molecular Biology 224
  • Organic Chemistry 71
  • Artificial Intelligence 55
  • Cancer Research 40
  • Plant Science 36
Replace Thanh Van Le with:
Thanh Van Le United States
Kieron Taylor United Kingdom
Bruno Iochins Grisci Brazil
Zhenyu Li China
Herman Midelfart Norway
Sébastien Géhant Switzerland
Jin Xie China
Xiaolong Qi China
Fabio De Bona relative to Thanh Van Le United States Thanh Van Le's profile →
Citations per field
00.5×
Thanh Van Le · 1×
Citations per year

Countries citing papers authored by Fabio De Bona

Since Specialization
Citations

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

Fields of papers citing papers by Fabio De Bona

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabio De Bona

This figure shows the co-authorship network connecting the top 25 collaborators of Fabio De Bona. A scholar is included among the top collaborators of Fabio De Bona based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fabio De Bona. Fabio De Bona is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
#WorkIndexed citations
1 3
2 17
3 43
4 35
5 37
6
Learning Dense Models of Query Similarity from User Click Logs
7
7 61
8
Simple Risk Bounds for Position-Sensitive Max-Margin Ranking Algorithms
1
9 68
10 64
11
RNA secondary structure prediction using large margin methods
0
12
SHOGUN - A Large Scale Machine Learning Toolbox
1
13 13
14 8

About Fabio De Bona

Fabio De Bona is a scholar working on Aging, Organic Chemistry and Pharmaceutical Science, having authored 14 papers that have together received 358 indexed citations. Recurring topics across this work include Catalytic C–H Functionalization Methods (5 papers), RNA and protein synthesis mechanisms (4 papers) and Genomics and Phylogenetic Studies (4 papers). The work is most often cited by research in Molecular Biology (224 citations), Cancer Research (40 citations) and Organic Chemistry (71 citations). Fabio De Bona has collaborated with scholars based in Germany, Italy and Switzerland. Frequent co-authors include Gunnar Rätsch, Korbinian Schneeberger, Stephan Ossowski, Luigino Troisi, Serena Perrone, Andreas Krause, Vipin T. Sreedharan, Géraldine Jean, André Kahles and Cheng Soon Ong. Their work appears in journals such as Bioinformatics, Chemical Communications and Genome Research.

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026