Nigel Duffy
- Artificial Intelligence top 1%
- Machine Learning and Algorithms 4
- Neural Networks and Applications 3
- Machine Learning and Data Classification 2
- Domain Adaptation and Few-Shot Learning 2
- Bayesian Modeling and Causal Inference 1
- Molecular Biology top 10%
- Machine Learning in Bioinformatics 1
- Biophysics top 5%
- Analytical Chemistry top 5%
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- Data Quality and Management 2
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- Sparse and Compressive Sensing Techniques 1
- Co-authors
- Nello CristianiniDavid HausslerTerrence S. FureyDavid BednarskiMichèl SchummerMichael CollinsDavid P. HelmboldJohn H. Griffin
- Journals
- Theoretical Computer Science (1 paper)Bioinformatics (1 paper)Bioorganic & Medicinal Chemistry Letters (1 paper)
- Partner nations
- United StatesUnited KingdomIsrael
In The Last Decade
Nigel Duffy
14 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 174
- Artificial Intelligence 920
- Computer Vision and Pattern Recognition 363
- Molecular Biology 1.2k
- Biophysics 56
- Analytical Chemistry 93
Countries citing papers authored by Nigel Duffy
This map shows the geographic impact of Nigel Duffy'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 Nigel Duffy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nigel Duffy more than expected).
Fields of papers citing papers by Nigel Duffy
This network shows the impact of papers produced by Nigel Duffy. 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 Nigel Duffy. The network helps show where Nigel Duffy may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Nigel Duffy, 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 | 2023 | 1 | |
| 2 | 2021 | 1 | |
| 3 | 2020 | 3 | |
| 4 | Document Enhancement System Using Auto-encoders | 2019 | 2 |
| 5 | 2017 | 5 | |
| 6 | 2011 | 17 | |
| 7 | Support Vector Machine | 2004 | 3 |
| 8 | 2002 | 13 | |
| 9 | 2002 | 120 | |
| 10 | 2002 | 1 | |
| 11 | 2001 | 332 | |
| 12 | Leveraging for Regression | 2000 | 18 |
| 13 | Support vector machine classification and validation of cancer tissue samples using microarray expression databreakdown → | 2000 | 1777 |
| 14 | Potential Boosters | 1999 | 27 |
About Nigel Duffy
Nigel Duffy is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Vision and Pattern Recognition, Computational Theory and Mathematics and Information Systems, having authored 14 papers that have together received 2.3k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (4 papers), Neural Networks and Applications (3 papers), Machine Learning and Data Classification (2 papers), Data Quality and Management (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Sparse and Compressive Sensing Techniques (1 paper), Machine Learning in Bioinformatics (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Artificial Intelligence (920 citations), Computer Vision and Pattern Recognition (363 citations), Molecular Biology (1.2k citations), Biophysics (56 citations) and Analytical Chemistry (93 citations). Nigel Duffy has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Nello Cristianini, David Haussler, Terrence S. Furey, David Bednarski, Michèl Schummer, Michael Collins, David P. Helmbold, John H. Griffin, Brian C. Raimundo and Carl Nathan. Their work appears in journals such as Theoretical Computer Science, Bioinformatics, Bioorganic & Medicinal Chemistry Letters, Machine Learning and IEEE Transactions on Knowledge and Data Engineering.
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.