David Campos
Impact in
- Toxicology top 10%
- Pharmacovigilance and Adverse Drug Reactions
- Artificial Intelligence top 5%
- Topic Modeling
- Semantic Web and Ontologies
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
Papers in
-
- Pharmacovigilance and Adverse Drug Reactions 2
-
- Semantic Web and Ontologies 3
- Topic Modeling 3
- Advanced Text Analysis Techniques 2
- Co-authors
- José Luís OliveiraSérgio MatosErik M. van MulligenJan A. KorsLaura I. FurlongAnna Bauer‐MehrenPaul AvillachGayo Diallo
- Journals
- BMC Bioinformatics (2 papers)Bioinformatics (2 papers)Journal of Cheminformatics (1 paper)Pharmacoepidemiology and Drug Safety (1 paper)PLoS ONE (1 paper)
- Partner nations
- PortugalNetherlandsFrance
In The Last Decade
David Campos
12 papers receiving 323 citations
Peers
Comparison fields: 5 of 68
- Toxicology 28
- Artificial Intelligence 213
- Molecular Biology 255
- Computational Theory and Mathematics 43
- Information Systems and Management 11
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-authorship network
The 22 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 | 2016 | 5 | |
| 2 | 2016 | 2 | |
| 3 | 2015 | 14 | |
| 4 | 2014 | 40 | |
| 5 | A fast rule-based approach for biomedical event extraction | 2013 | 37 |
| 6 | 2013 | 47 | |
| 7 | 2013 | 17 | |
| 8 | 2013 | 73 | |
| 9 | 2013 | 56 | |
| 10 | 2012 | 12 | |
| 11 | 2012 | 33 | |
| 12 | 2010 | 1 |
About David Campos
David Campos is a scholar working on Toxicology, Artificial Intelligence, Health Information Management, Computational Theory and Mathematics and Genetics, having authored 12 papers that have together received 337 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (8 papers), Genomics and Rare Diseases (4 papers), Semantic Web and Ontologies (3 papers), Computational Drug Discovery Methods (3 papers), Topic Modeling (3 papers), Advanced Text Analysis Techniques (2 papers), Bioinformatics and Genomic Networks (2 papers) and Pharmacovigilance and Adverse Drug Reactions (2 papers). The work is most often cited by research in Toxicology (28 citations), Artificial Intelligence (213 citations), Molecular Biology (255 citations), Computational Theory and Mathematics (43 citations) and Information Systems and Management (11 citations). David Campos has collaborated with scholars based in Portugal, Netherlands and France. Frequent co-authors include José Luís Oliveira, Sérgio Matos, Erik M. van Mulligen, Jan A. Kors, Laura I. Furlong, Anna Bauer‐Mehren, Paul Avillach, Gayo Diallo, Johan van der Lei and Pedro Lopes. Their work appears in journals such as BMC Bioinformatics, Bioinformatics, Journal of Cheminformatics, Pharmacoepidemiology and Drug Safety 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.