Cam Macdonell
Impact in
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- Machine Learning in Bioinformatics
- Genomics and Phylogenetic Studies
- RNA and protein synthesis mechanisms
- Protein Structure and Dynamics
- Biochemical and Structural Characterization
- Bioinformatics and Genomic Networks
- Computer Science Applications top 10%
Papers in
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- Open Source Software Innovations 2
- Online Learning and Analytics 2
- Co-authors
- David Scott Wishart (6 shared papers)Ping Lu (4 shared papers)Brett Poulin (4 shared papers)Russell Greiner (4 shared papers)Duane Szafron (4 shared papers)Roman Eisner (4 shared papers)John Anvik (3 shared papers)Zhonghua Lu (2 shared papers)
- Journals
- Nucleic Acids Research (3 papers)Journal of Parallel and Distributed Computing (1 paper)Bioinformatics (1 paper)Innovative Applications of Artificial Intelligence (1 paper)ACM SIGCAS Computers and Society (1 paper)
- Partner nations
- CanadaUnited StatesMexico
In The Last Decade
Cam Macdonell
10 papers receiving 604 citations
Peers
Comparison fields: 5 of 107
- Molecular Biology 375
- Computer Science Applications 26
- Health Informatics 6
- Artificial Intelligence 92
- Spectroscopy 39
Countries citing papers authored by Cam Macdonell
This map shows the geographic impact of Cam Macdonell'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 Cam Macdonell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cam Macdonell more than expected).
Fields of papers citing papers by Cam Macdonell
This network shows the impact of papers produced by Cam Macdonell. 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 Cam Macdonell. The network helps show where Cam Macdonell may publish in the future.
Co-authors
The 25 scholars most cited alongside Cam Macdonell, 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 | 2004 | 289 | |
| 2 | 2004 | 92 | |
| 3 | 2010 | 75 | |
| 4 | Visual explanation of evidence in additive classifiers | 2006 | 69 |
| 5 | 2009 | 44 | |
| 6 | 2015 | 25 | |
| 7 | 2015 | 10 | |
| 8 | 2003 | 10 | |
| 9 | 2020 | 2 | |
| 10 | 2010 | 1 | |
| 11 | 2006 | 1 | |
| 12 | 2020 | 0 |
About Cam Macdonell
Cam Macdonell is a scholar working on Computer Science Applications, Developmental and Educational Psychology, Computer Networks and Communications, Information Systems and Management and Communication, having authored 12 papers that have together received 618 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (2 papers), Advanced Data Storage Technologies (2 papers), Machine Learning in Bioinformatics (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Protein Structure and Dynamics (2 papers), Open Source Software Innovations (2 papers), Enzyme Structure and Function (2 papers) and Online Learning and Analytics (2 papers). The work is most often cited by research in Molecular Biology (375 citations), Computer Science Applications (26 citations), Health Informatics (6 citations), Artificial Intelligence (92 citations) and Spectroscopy (39 citations). Cam Macdonell has collaborated with scholars based in Canada, United States and Mexico. Frequent co-authors include David Scott Wishart, Ping Lu, Brett Poulin, Russell Greiner, Duane Szafron, Roman Eisner, John Anvik, Zhonghua Lu, Alona Fyshe and Patrick Tang. Their work appears in journals such as Nucleic Acids Research, Journal of Parallel and Distributed Computing, Bioinformatics, Innovative Applications of Artificial Intelligence and ACM SIGCAS Computers and Society.
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.