Ole Lund
Impact in
- Molecular Medicine top 0.01%
- Antibiotic Resistance in Bacteria
- Endocrinology top 0.02%
- Escherichia coli research studies
Papers in
-
- vaccines and immunoinformatics approaches 74
- Genomics and Phylogenetic Studies 29
- Immunology 63
- Immunotherapy and Immune Responses 35
- T-cell and B-cell Immunology 26
- Co-authors
- Morten Nielsen (79 shared papers)Frank M. Aarestrup (50 shared papers)Henrik Hasman (17 shared papers)Claus Lundegaard (39 shared papers)Ea Zankari (6 shared papers)Mette Voldby Larsen (25 shared papers)Salvatore Cosentino (6 shared papers)Søren Buus (36 shared papers)
- Journals
- PLoS ONE (27 papers)Immunogenetics (10 papers)Journal of Clinical Microbiology (9 papers)Bioinformatics (9 papers)Scientific Reports (8 papers)
- Partner nations
- DenmarkUnited StatesSweden
In The Last Decade
Ole Lund
332 papers receiving 37.3k citations
Ole Lund's Hit Papers
Peers
Comparison fields: 5 of 183
- Molecular Medicine 7.6k
- Endocrinology 4.9k
- Immunology 7.3k
- Infectious Diseases 5.5k
- Clinical Biochemistry 1.8k
Countries citing papers authored by Ole Lund
This map shows the geographic impact of Ole Lund'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 Ole Lund with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ole Lund more than expected).
Fields of papers citing papers by Ole Lund
This network shows the impact of papers produced by Ole Lund. 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 Ole Lund. The network helps show where Ole Lund may publish in the future.
Co-authors
The 25 scholars most cited alongside Ole Lund, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 335 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Identification of acquired antimicrobial resistance genes Hit paper breakdown → | 2012 | 3914 |
| 2 | In Silico Detection and Typing of Plasmids using PlasmidFinder and Plasmid Multilocus Sequence Typing Hit paper breakdown → | 2014 | 3463 |
| 3 | Multilocus Sequence Typing of Total-Genome-Sequenced Bacteria Hit paper breakdown → | 2012 | 1796 |
| 4 | Real-Time Whole-Genome Sequencing for Routine Typing, Surveillance, and Outbreak Detection of Verotoxigenic Escherichia coli Hit paper breakdown → | 2014 | 1060 |
| 5 | Improved method for predicting linear B-cell epitopes. Hit paper breakdown → | 2006 | 1014 |
| 6 | Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations Hit paper breakdown → | 2003 | 865 |
| 7 | Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System Hit paper breakdown → | 2010 | 782 |
| 8 | Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction Hit paper breakdown → | 2007 | 755 |
| 9 | Solving the Problem of Comparing Whole Bacterial Genomes across Different Sequencing Platforms Hit paper breakdown → | 2014 | 695 |
| 10 | NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11 Hit paper breakdown → | 2008 | 618 |
| 11 | PointFinder: a novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens Hit paper breakdown → | 2017 | 581 |
| 12 | Rapid and precise alignment of raw reads against redundant databases with KMA Hit paper breakdown → | 2018 | 579 |
| 13 | NetMHCpan, a method for MHC class I binding prediction beyond humans Hit paper breakdown → | 2008 | 563 |
| 14 | Peptide binding predictions for HLA DR, DP and DQ molecules Hit paper breakdown → | 2010 | 537 |
| 15 | Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking Hit paper breakdown → | 2012 | 527 |
| 16 | Prediction of residues in discontinuous B‐cell epitopes using protein 3D structures Hit paper breakdown → | 2006 | 511 |
| 17 | Insights from 20 years of bacterial genome sequencing Hit paper breakdown → | 2015 | 508 |
| 18 | 2007 | 488 | |
| 19 | PathogenFinder - Distinguishing Friend from Foe Using Bacterial Whole Genome Sequence Data Hit paper breakdown → | 2013 | 468 |
| 20 | 2007 | 466 |
About Ole Lund
Ole Lund is a scholar working on Molecular Biology, Immunology, Cardiology and Cardiovascular Medicine, Epidemiology and Plant Science, having authored 335 papers that have together received 38.0k indexed citations. Recurring topics across this work include vaccines and immunoinformatics approaches (74 papers), Cardiac Valve Diseases and Treatments (37 papers), Immunotherapy and Immune Responses (35 papers), Genomics and Phylogenetic Studies (29 papers), Monoclonal and Polyclonal Antibodies Research (28 papers), T-cell and B-cell Immunology (26 papers), HIV Research and Treatment (24 papers) and Bacteriophages and microbial interactions (21 papers). The work is most often cited by research in Molecular Medicine (7.6k citations), Endocrinology (4.9k citations), Immunology (7.3k citations), Infectious Diseases (5.5k citations) and Clinical Biochemistry (1.8k citations). Ole Lund has collaborated with scholars based in Denmark, United States and Sweden. Frequent co-authors include Morten Nielsen, Frank M. Aarestrup, Henrik Hasman, Claus Lundegaard, Ea Zankari, Mette Voldby Larsen, Salvatore Cosentino, Søren Buus, Simon Rasmussen and Mikkel V. Larsen. Their work appears in journals such as PLoS ONE, Immunogenetics, Journal of Clinical Microbiology, Bioinformatics and Scientific Reports.
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.