Daniel Teixeira
Impact in
- Spectroscopy top 5%
- Advanced Proteomics Techniques and Applications
- Mass Spectrometry Techniques and Applications
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- Machine Learning in Bioinformatics
- RNA and protein synthesis mechanisms
- Bioinformatics and Genomic Networks
- Genomics and Phylogenetic Studies
- Metabolomics and Mass Spectrometry Studies
- Biomedical Text Mining and Ontologies
Papers in
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- Biomedical Text Mining and Ontologies 2
- Bioinformatics and Genomic Networks 2
- Machine Learning in Bioinformatics 1
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- Advanced Proteomics Techniques and Applications 4
- Co-authors
- Monique Zahn‐Zabal (5 shared papers)Pierre-André Michel (5 shared papers)Alain Gateau (4 shared papers)Lydie Lane (4 shared papers)Pascale Gaudet (5 shared papers)Amos Bairoch (4 shared papers)Paula Duek (3 shared papers)Valentine Rech de Laval (4 shared papers)
- Journals
- Nucleic Acids Research (3 papers)Human Genomics (1 paper)Bioinformatics (1 paper)Eurosurveillance (1 paper)Scientific Data (1 paper)
- Partner nations
- SwitzerlandPortugalHungary
In The Last Decade
Daniel Teixeira
8 papers receiving 407 citations
Peers
Comparison fields: 5 of 72
- Spectroscopy 158
- Molecular Biology 320
- Genetics 41
- Cancer Research 20
- Information Systems and Management 9
Countries citing papers authored by Daniel Teixeira
This map shows the geographic impact of Daniel Teixeira'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 Daniel Teixeira with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Teixeira more than expected).
Fields of papers citing papers by Daniel Teixeira
This network shows the impact of papers produced by Daniel Teixeira. 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 Daniel Teixeira. The network helps show where Daniel Teixeira may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Teixeira, 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 | 2019 | 140 | |
| 2 | 2016 | 122 | |
| 3 | 2015 | 68 | |
| 4 | 2017 | 52 | |
| 5 | 2023 | 12 | |
| 6 | 2018 | 8 | |
| 7 | 2018 | 4 | |
| 8 | 2010 | 3 | |
| 9 | An ontology-mapping service for agent-based automated negotiation | 2008 | 1 |
| 10 | 2025 | 0 |
About Daniel Teixeira
Daniel Teixeira is a scholar working on Molecular Biology, Spectroscopy, Genetics, Information Systems and Artificial Intelligence, having authored 10 papers that have together received 410 indexed citations. Recurring topics across this work include Advanced Proteomics Techniques and Applications (4 papers), Semantic Web and Ontologies (2 papers), Biomedical Text Mining and Ontologies (2 papers), Bioinformatics and Genomic Networks (2 papers), Genomics and Rare Diseases (2 papers), Nutrition, Genetics, and Disease (1 paper), Antimicrobial Resistance in Staphylococcus (1 paper) and Machine Learning in Bioinformatics (1 paper). The work is most often cited by research in Spectroscopy (158 citations), Molecular Biology (320 citations), Genetics (41 citations), Cancer Research (20 citations) and Information Systems and Management (9 citations). Daniel Teixeira has collaborated with scholars based in Switzerland, Portugal and Hungary. Frequent co-authors include Monique Zahn‐Zabal, Pierre-André Michel, Alain Gateau, Lydie Lane, Pascale Gaudet, Amos Bairoch, Paula Duek, Valentine Rech de Laval, Frédéric Nikitin and Isabelle Cusin. Their work appears in journals such as Nucleic Acids Research, Human Genomics, Bioinformatics, Eurosurveillance and Scientific Data.
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.