Michael Mathieson
Impact in
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- Computational Drug Discovery Methods
- Artificial Intelligence top 10%
- Machine Learning and Algorithms
- Algorithms and Data Compression
- Machine Learning and Data Classification
Papers in
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- Machine Learning and Algorithms 2
- Algorithms and Data Compression 1
- Co-authors
- Christian Lemmen (4 shared papers)Jun Liao (3 shared papers)Gunnar Rätsch (3 shared papers)Manfred K. Warmuth (3 shared papers)Santosh Putta (2 shared papers)D. G. Catcheside (1 shared paper)Joëlle Prunet (1 shared paper)Diego Gamba‐Sánchez (1 shared paper)
- Journals
- Organic & Biomolecular Chemistry (1 paper)Annals of Botany (1 paper)Journal of Chemical Information and Computer Sciences (1 paper)Journal of General Microbiology (1 paper)ChemInform (1 paper)
- Partner nations
- United StatesAustraliaUnited Kingdom
In The Last Decade
Michael Mathieson
7 papers receiving 342 citations
Peers
Comparison fields: 5 of 82
- Computational Theory and Mathematics 169
- Artificial Intelligence 98
- Spectroscopy 40
- Molecular Biology 155
- Analytical Chemistry 15
Countries citing papers authored by Michael Mathieson
This map shows the geographic impact of Michael Mathieson'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 Michael Mathieson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Mathieson more than expected).
Fields of papers citing papers by Michael Mathieson
This network shows the impact of papers produced by Michael Mathieson. 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 Michael Mathieson. The network helps show where Michael Mathieson may publish in the future.
Co-authors
The 11 scholars most cited alongside Michael Mathieson, 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 | 2003 | 257 | |
| 2 | 1955 | 45 | |
| 3 | Active Learning in the Drug Discovery Process | 2001 | 42 |
| 4 | 2016 | 13 | |
| 5 | 2003 | 6 | |
| 6 | Support Vector Machines for Active Learning in the Drug Discovery Process | 2003 | 4 |
| 7 | 1956 | 2 |
About Michael Mathieson
Michael Mathieson is a scholar working on Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics, Spectroscopy and Organic Chemistry, having authored 7 papers that have together received 369 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (2 papers), Analytical Chemistry and Chromatography (2 papers), Computational Drug Discovery Methods (2 papers), Hermeneutics and Narrative Identity (1 paper), Fluorine in Organic Chemistry (1 paper), Fungal Biology and Applications (1 paper), Analytical Methods in Pharmaceuticals (1 paper) and Algorithms and Data Compression (1 paper). The work is most often cited by research in Computational Theory and Mathematics (169 citations), Artificial Intelligence (98 citations), Spectroscopy (40 citations), Molecular Biology (155 citations) and Analytical Chemistry (15 citations). Michael Mathieson has collaborated with scholars based in United States, Australia and United Kingdom. Frequent co-authors include Christian Lemmen, Jun Liao, Gunnar Rätsch, Manfred K. Warmuth, Santosh Putta, D. G. Catcheside, Joëlle Prunet, Diego Gamba‐Sánchez, Elodie Brun and Louis J. Farrugia. Their work appears in journals such as Organic & Biomolecular Chemistry, Annals of Botany, Journal of Chemical Information and Computer Sciences, Journal of General Microbiology and ChemInform.
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.