Alexander D. Diehl
- Immunology top 5%
- Immune Cell Function and Interaction 5
- T-cell and B-cell Immunology 3
- Biophysics top 5%
- Cell Image Analysis Techniques 6
-
- Biomedical Text Mining and Ontologies 24
- Bioinformatics and Genomic Networks 16
- vaccines and immunoinformatics approaches 4
- Artificial Intelligence top 10%
- Semantic Web and Ontologies 5
-
- Genomics and Rare Diseases 7
- Co-authors
- Hans‐Gustaf LjunggrenJudith A. BlakeBenedict J. ChambersChris MungallTerrence F. MeehanNatalio GarbiFrank MomburgLuc Van Kaer
- Partner nations
- United StatesSwedenUnited Kingdom
In The Last Decade
Alexander D. Diehl
34 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 109
- Immunology 447
- Biophysics 99
- Molecular Biology 611
- Artificial Intelligence 138
- Virology 16
Countries citing papers authored by Alexander D. Diehl
This map shows the geographic impact of Alexander D. Diehl'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 Alexander D. Diehl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander D. Diehl more than expected).
Fields of papers citing papers by Alexander D. Diehl
This network shows the impact of papers produced by Alexander D. Diehl. 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 Alexander D. Diehl. The network helps show where Alexander D. Diehl may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Alexander D. Diehl, 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 | 2022 | 11 | |
| 2 | 2019 | 2 | |
| 3 | 2019 | 7 | |
| 4 | 2019 | 10 | |
| 5 | 2018 | 33 | |
| 6 | Applications of OBI 'assay' | 2014 | 2 |
| 7 | An ontological representation and analysis of patient- reported and clinical outcomes for multiple sclerosis | 2014 | 4 |
| 8 | The Ocular Disease Ontology. | 2013 | 1 |
| 9 | Measuring Cognitive Functions: Hurdles in the Development of the NeuroPsychological Testing Ontology. | 2013 | 10 |
| 10 | 2013 | 9 | |
| 11 | Representing Disease Courses: An Application of the Neurological Disease Ontology to Multiple Sclerosis Typology | 2013 | 1 |
| 12 | 2012 | 28 | |
| 13 | Revising the Cell Ontology. | 2011 | 1 |
| 14 | 2011 | 32 | |
| 15 | 2011 | 96 | |
| 16 | 2010 | 26 | |
| 17 | 2009 | 17 | |
| 18 | 2008 | 27 | |
| 19 | 2000 | 95 | |
| 20 | 1999 | 41 |
About Alexander D. Diehl
Alexander D. Diehl is a scholar working on Anatomy, Biophysics and Molecular Biology, having authored 34 papers that have together received 1.1k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (24 papers), Bioinformatics and Genomic Networks (16 papers), Genomics and Rare Diseases (7 papers), Cell Image Analysis Techniques (6 papers), Semantic Web and Ontologies (5 papers), Immune Cell Function and Interaction (5 papers), vaccines and immunoinformatics approaches (4 papers) and T-cell and B-cell Immunology (3 papers). The work is most often cited by research in Immunology (447 citations), Biophysics (99 citations) and Molecular Biology (611 citations). Alexander D. Diehl has collaborated with scholars based in United States, Sweden and United Kingdom. Frequent co-authors include Hans‐Gustaf Ljunggren, Judith A. Blake, Benedict J. Chambers, Chris Mungall, Terrence F. Meehan, Natalio Garbi, Frank Momburg, Hans‐Gustaf Ljunggren, Luc Van Kaer and Richard H. Scheuermann. Their work appears in journals such as Nature Immunology, Bioinformatics and The Journal of Immunology.
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.