Ian Walsh
Impact in
- Molecular Biology top 5%
- Protein Structure and Dynamics
- Machine Learning in Bioinformatics
- RNA and protein synthesis mechanisms
- Glycosylation and Glycoproteins Research
- Genomics and Phylogenetic Studies
- Bioinformatics and Genomic Networks
- RNA Research and Splicing
- Spectroscopy top 5%
Papers in
-
- Protein Structure and Dynamics 22
- Glycosylation and Glycoproteins Research 17
- Viral Infectious Diseases and Gene Expression in Insects 12
- Machine Learning in Bioinformatics 10
- Genomics and Phylogenetic Studies 10
- Bioinformatics and Genomic Networks 7
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- Monoclonal and Polyclonal Antibodies Research 11
- Co-authors
- Silvio C. E. TosattoTomás Di DomenicoAlberto J. M. MartínFlavio SenoAntonio TrovatoGianluca PollastriAlessandro VulloCarlo Ferrari
In The Last Decade
Ian Walsh
52 papers receiving 2.5k citations
Peers
Comparison fields: 5 of 124
- Molecular Biology 2.2k
- Spectroscopy 211
- Materials Chemistry 510
- Aging 16
- Computational Theory and Mathematics 144
Countries citing papers authored by Ian Walsh
This map shows the geographic impact of Ian Walsh'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 Ian Walsh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ian Walsh more than expected).
Fields of papers citing papers by Ian Walsh
This network shows the impact of papers produced by Ian Walsh. 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 Ian Walsh. The network helps show where Ian Walsh may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ian Walsh, 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 | 2025 | 0 | |
| 2 | 2024 | 2 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 22 | |
| 5 | 2023 | 3 | |
| 6 | 2021 | 4 | |
| 7 | 2020 | 5 | |
| 8 | 2020 | 31 | |
| 9 | 2019 | 17 | |
| 10 | Plasma N-glycans in colorectal cancer risk | 2018 | 8 |
| 11 | 2016 | 29 | |
| 12 | 2014 | 154 | |
| 13 | 2013 | 27 | |
| 14 | 2013 | 28 | |
| 15 | 2011 | 70 | |
| 16 | 2011 | 2 | |
| 17 | 2009 | 14 | |
| 18 | 2006 | 76 | |
| 19 | 2003 | 32 | |
| 20 | 2000 | 8 |
About Ian Walsh
Ian Walsh is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging, Complementary and Manual Therapy, Spectroscopy and Materials Chemistry, having authored 57 papers that have together received 2.5k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (22 papers), Glycosylation and Glycoproteins Research (17 papers), Enzyme Structure and Function (15 papers), Viral Infectious Diseases and Gene Expression in Insects (12 papers), Monoclonal and Polyclonal Antibodies Research (11 papers), Machine Learning in Bioinformatics (10 papers), Genomics and Phylogenetic Studies (10 papers) and Bioinformatics and Genomic Networks (7 papers). The work is most often cited by research in Molecular Biology (2.2k citations), Spectroscopy (211 citations), Materials Chemistry (510 citations), Aging (16 citations) and Computational Theory and Mathematics (144 citations). Ian Walsh has collaborated with scholars based in Singapore, Italy and Ireland. Frequent co-authors include Silvio C. E. Tosatto, Tomás Di Domenico, Alberto J. M. Martín, Flavio Seno, Antonio Trovato, Gianluca Pollastri, Alessandro Vullo, Carlo Ferrari, Manuel Giollo and Giovanni Minervini. Their work appears in journals such as Bioinformatics, BMC Bioinformatics, Nucleic Acids Research, Scientific Reports and Biotechnology Journal.
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.