Matthew Parry
Impact in
- Horticulture top 10%
- Modeling and Simulation top 5%
- COVID-19 epidemiological studies
Papers in
-
- Epigenetics and DNA Methylation 6
- Machine Learning in Bioinformatics 2
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- Target Tracking and Data Fusion in Sensor Networks 2
- Co-authors
- Aniruddha Chatterjee (9 shared papers)Peter A. Stockwell (7 shared papers)Euan J. Rodger (6 shared papers)Dominic Malcolm (1 shared paper)Alice Grady (1 shared paper)Lisa Parker (1 shared paper)Tim Chambers (1 shared paper)Lisa Bero (1 shared paper)
- Journals
- Electronic Journal of Statistics (2 papers)Epigenomics (2 papers)Sensors (2 papers)Proceedings of the National Academy of Sciences (1 paper)Pathology (1 paper)
- Partner nations
- New ZealandUnited StatesUnited Kingdom
In The Last Decade
Matthew Parry
41 papers receiving 661 citations
Peers
Comparison fields: 5 of 134
- Horticulture 15
- Modeling and Simulation 49
- Ecological Modeling 23
- Information Systems 99
- Gender Studies 33
Countries citing papers authored by Matthew Parry
This map shows the geographic impact of Matthew Parry'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 Parry with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Parry more than expected).
Fields of papers citing papers by Matthew Parry
This network shows the impact of papers produced by Matthew Parry. 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 Parry. The network helps show where Matthew Parry may publish in the future.
Co-authors
The 25 scholars most cited alongside Matthew Parry, 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 45 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 147 | |
| 2 | 2018 | 71 | |
| 3 | 2015 | 60 | |
| 4 | 2014 | 46 | |
| 5 | 2007 | 40 | |
| 6 | 2004 | 39 | |
| 7 | 2016 | 33 | |
| 8 | 2018 | 33 | |
| 9 | 2022 | 26 | |
| 10 | 2023 | 21 | |
| 11 | 2018 | 18 | |
| 12 | 2021 | 14 | |
| 13 | 2020 | 14 | |
| 14 | 2017 | 11 | |
| 15 | 2016 | 11 | |
| 16 | 2016 | 9 | |
| 17 | 2021 | 8 | |
| 18 | 2021 | 6 | |
| 19 | 2020 | 6 | |
| 20 | 2000 | 6 |
About Matthew Parry
Matthew Parry is a scholar working on Molecular Biology, Artificial Intelligence, Ecology, Statistics and Probability and Ecology, Evolution, Behavior and Systematics, having authored 45 papers that have together received 677 indexed citations. Recurring topics across this work include Epigenetics and DNA Methylation (6 papers), Plant Virus Research Studies (3 papers), Advanced Statistical Methods and Models (3 papers), Immune cells in cancer (2 papers), Plant and animal studies (2 papers), Target Tracking and Data Fusion in Sensor Networks (2 papers), Machine Learning in Bioinformatics (2 papers) and Phytoplasmas and Hemiptera pathogens (2 papers). The work is most often cited by research in Horticulture (15 citations), Modeling and Simulation (49 citations), Ecological Modeling (23 citations), Information Systems (99 citations) and Gender Studies (33 citations). Matthew Parry has collaborated with scholars based in New Zealand, United States and United Kingdom. Frequent co-authors include Aniruddha Chatterjee, Peter A. Stockwell, Euan J. Rodger, Dominic Malcolm, Alice Grady, Lisa Parker, Tim Chambers, Lisa Bero, Theresa HM Moore and Ella Flemyng. Their work appears in journals such as Electronic Journal of Statistics, Epigenomics, Sensors, Proceedings of the National Academy of Sciences and Pathology.
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.