Alberto Paccanaro
- Molecular Biology top 5%
- Computational Theory and Mathematics top 1%
- Artificial Intelligence top 5%
- Plant Science top 10%
- Genetics top 10%
- Co-authors
- Haiyuan YuTamás NepuszMark GersteinDiego GaleanoAlfonso E. RomeroGeoffrey E. HintonM SnyderValery Trifonov
- Topics
- Bioinformatics and Genomic Networks (24 papers)Machine Learning in Bioinformatics (11 papers)Gene expression and cancer classification (10 papers)
- Partner nations
- United KingdomUnited StatesBrazil
In The Last Decade
Alberto Paccanaro
42 papers receiving 2.5k citations
Hit Papers
Peers
Comparison fields: 5 of 157
- Molecular Biology 1.9k
- Computational Theory and Mathematics 530
- Artificial Intelligence 234
- Plant Science 232
- Genetics 196
Countries citing papers authored by Alberto Paccanaro
This map shows the geographic impact of Alberto Paccanaro'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 Alberto Paccanaro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alberto Paccanaro more than expected).
Fields of papers citing papers by Alberto Paccanaro
This network shows the impact of papers produced by Alberto Paccanaro. 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 Alberto Paccanaro. The network helps show where Alberto Paccanaro may publish in the future.
Co-authorship network of co-authors of Alberto Paccanaro
This figure shows the co-authorship network connecting the top 25 collaborators of Alberto Paccanaro. A scholar is included among the top collaborators of Alberto Paccanaro 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 Alberto Paccanaro. Alberto Paccanaro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 27 | |
| 4 | 31 | |
| 5 | 7 | |
| 6 | 3 | |
| 7 | 14 | |
| 8 | 41 | |
| 9 | Detecting overlapping protein complexes in protein-protein interaction networksbreakdown → | 919 |
| 10 | 87 | |
| 11 | 81 | |
| 12 | 2 | |
| 13 | 43 | |
| 14 | 139 | |
| 15 | 46 | |
| 16 | 122 | |
| 17 | 119 | |
| 18 | 29 | |
| 19 | Learning Hierarchical Structures with Linear Relational Embedding | 5 |
| 20 | Learning Distributed Representations by Mapping Concepts and Relations into a Linear Space | 3 |
About Alberto Paccanaro
Alberto Paccanaro is a scholar working on Computational Theory and Mathematics, Molecular Biology and Biological Psychiatry, having authored 45 papers that have together received 2.6k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (24 papers), Machine Learning in Bioinformatics (11 papers) and Gene expression and cancer classification (10 papers). The work is most often cited by research in Computational Theory and Mathematics (530 citations), Molecular Biology (1.9k citations) and Toxicology (38 citations). Alberto Paccanaro has collaborated with scholars based in United Kingdom, United States and Brazil. Frequent co-authors include Haiyuan Yu, Tamás Nepusz, Mark Gerstein, Diego Galeano, Alfonso E. Romero, Geoffrey E. Hinton, M Snyder, Valery Trifonov, Yu Xia and Long Lu. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.
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.