David Campos
Impact in
- Toxicology top 10%
- Pharmacovigilance and Adverse Drug Reactions
- Artificial Intelligence top 10%
- Topic Modeling
- Semantic Web and Ontologies
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
Papers in
-
- Biomedical Text Mining and Ontologies 8
- Machine Learning in Bioinformatics 1
- Fractal and DNA sequence analysis 1
-
- Algorithms and Data Compression 1
- Co-authors
- José Luís Oliveira (11 shared papers)Sérgio Matos (8 shared papers)Jan A. Kors (3 shared papers)Erik M. van Mulligen (3 shared papers)Jordi Mestres (2 shared papers)Gayo Diallo (2 shared papers)Paul Avillach (2 shared papers)Laura I. Furlong (2 shared papers)
- Journals
- BMC Bioinformatics (2 papers)Bioinformatics (2 papers)Pharmacoepidemiology and Drug Safety (1 paper)Database (1 paper)PLoS ONE (1 paper)
- Partner nations
- PortugalNetherlandsSpain
In The Last Decade
David Campos
12 papers receiving 326 citations
Peers
Comparison fields: 5 of 63
- Toxicology 27
- Artificial Intelligence 158
- Molecular Biology 228
- Computational Theory and Mathematics 30
- Information Systems and Management 10
Countries citing papers authored by David Campos
This map shows the geographic impact of David Campos'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 David Campos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Campos more than expected).
Fields of papers citing papers by David Campos
This network shows the impact of papers produced by David Campos. 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 David Campos. The network helps show where David Campos may publish in the future.
Co-authors
The 21 scholars most cited alongside David Campos, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 74 | |
| 2 | 2013 | 56 | |
| 3 | 2013 | 47 | |
| 4 | 2014 | 40 | |
| 5 | A fast rule-based approach for biomedical event extraction | 2013 | 37 |
| 6 | 2012 | 34 | |
| 7 | 2013 | 17 | |
| 8 | 2015 | 14 | |
| 9 | 2012 | 12 | |
| 10 | 2016 | 5 | |
| 11 | 2016 | 2 | |
| 12 | 2010 | 1 |
About David Campos
David Campos is a scholar working on Molecular Biology, Artificial Intelligence, Toxicology, Computer Networks and Communications and Information Systems, having authored 12 papers that have together received 339 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (8 papers), Pharmacovigilance and Adverse Drug Reactions (2 papers), Recommender Systems and Techniques (1 paper), Genomics and Rare Diseases (1 paper), Machine Learning in Bioinformatics (1 paper), Computational Drug Discovery Methods (1 paper), Fractal and DNA sequence analysis (1 paper) and Algorithms and Data Compression (1 paper). The work is most often cited by research in Toxicology (27 citations), Artificial Intelligence (158 citations), Molecular Biology (228 citations), Computational Theory and Mathematics (30 citations) and Information Systems and Management (10 citations). David Campos has collaborated with scholars based in Portugal, Netherlands and Spain. Frequent co-authors include José Luís Oliveira, Sérgio Matos, Jan A. Kors, Erik M. van Mulligen, Jordi Mestres, Gayo Diallo, Paul Avillach, Laura I. Furlong, Ernst Ahlberg and Ferrán Sanz. Their work appears in journals such as BMC Bioinformatics, Bioinformatics, Pharmacoepidemiology and Drug Safety, Database and PLoS ONE.
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.