Matthew R. Laird
- Molecular Biology top 2%
- Ecology top 2%
- Immunology top 5%
- Plant Science top 5%
- Infectious Diseases top 5%
- Co-authors
- Fiona S. L. BrinkmanRaymond LoGeoffrey L. WinsorMartin EsterNancy YuJames WagnerLeonard J. FosterS. Cenk Şahinalp
- Topics
- Genomics and Phylogenetic Studies (13 papers)Machine Learning in Bioinformatics (6 papers)RNA and protein synthesis mechanisms (5 papers)
- Partner nations
- CanadaUnited KingdomUnited States
In The Last Decade
Matthew R. Laird
17 papers receiving 5.2k citations
Hit Papers
Peers
Comparison fields: 5 of 147
- Molecular Biology 3.3k
- Ecology 1.1k
- Immunology 567
- Plant Science 564
- Infectious Diseases 547
Countries citing papers authored by Matthew R. Laird
This map shows the geographic impact of Matthew R. Laird'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 Matthew R. Laird with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew R. Laird more than expected).
Fields of papers citing papers by Matthew R. Laird
This network shows the impact of papers produced by Matthew R. Laird. 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 Matthew R. Laird. The network helps show where Matthew R. Laird may publish in the future.
Co-authorship network of co-authors of Matthew R. Laird
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew R. Laird. A scholar is included among the top collaborators of Matthew R. Laird based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthew R. Laird. Matthew R. Laird is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 23 | |
| 3 | 16 | |
| 4 | IslandViewer 4: expanded prediction of genomic islands for larger-scale datasetsbreakdown → | 1080 |
| 5 | 34 | |
| 6 | 57 | |
| 7 | 239 | |
| 8 | 13 | |
| 9 | 91 | |
| 10 | InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curationbreakdown → | 858 |
| 11 | 56 | |
| 12 | 15 | |
| 13 | 71 | |
| 14 | 62 | |
| 15 | PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotesbreakdown → | 1935 |
| 16 | 72 | |
| 17 | PSORTb v.2.0: Expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysisbreakdown → | 610 |
About Matthew R. Laird
Matthew R. Laird is a scholar working on Molecular Biology, Food Science and Ecology, having authored 17 papers that have together received 5.2k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (13 papers), Machine Learning in Bioinformatics (6 papers) and RNA and protein synthesis mechanisms (5 papers). The work is most often cited by research in Endocrinology (499 citations), Microbiology (472 citations) and Molecular Medicine (365 citations). Matthew R. Laird has collaborated with scholars based in Canada, United Kingdom and United States. Frequent co-authors include Fiona S. L. Brinkman, Raymond Lo, Geoffrey L. Winsor, Martin Ester, Nancy Yu, James Wagner, Leonard J. Foster, S. Cenk Şahinalp, Phuong Dao and Gabor Melli. Their work appears in journals such as Nucleic Acids Research, Bioinformatics and BMC Bioinformatics.
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.