Cam Macdonell

757 citations
13 papers · 586 · h-index 8

Impact in

    • 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

Papers in

Cam Macdonell

11 papers receiving 569 citations

Peers

Cam Macdonell
Comparison fields: 5 of 105
  • Molecular Biology 376
  • Computer Science Applications 26
  • Health Informatics 6
  • Artificial Intelligence 83
  • Microbiology 16
Replace Xosé M. Fernández with:
Xosé M. Fernández United Kingdom
Brett Poulin Canada
Didier Devaurs United States
Graham Cameron United Kingdom
Wayne Aubrey United Kingdom
Dana Movshovitz‐Attias United States
William Hayes United States
Yimeng Dou United States
Cornelia Hedeler United Kingdom
Cath Brooksbank United Kingdom
Cam Macdonell relative to Xosé M. Fernández United Kingdom Xosé M. Fernández's profile →
Citations per field
00.5×4.3×
Xosé M. Fernández · 1×
Citations per year

Countries citing papers authored by Cam Macdonell

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Border = papers with Cam Macdonell Line = papers co-authored together Cam Macdonell links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 2004276
2 200488
3 201067
4
Visual explanation of evidence in additive classifiers
200657
5 200943
6 201525
7 201510
8 200310
9 20036
10 20202
11 20101
12 20061
13 20200

About Cam Macdonell

Cam Macdonell is a scholar working on Molecular Biology, Computer Science Applications, Computer Networks and Communications, Artificial Intelligence and Spectroscopy, having authored 13 papers that have together received 586 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (3 papers), Online Learning and Analytics (2 papers), Open Source Software Innovations (2 papers), RNA and protein synthesis mechanisms (2 papers), Genomics and Phylogenetic Studies (2 papers), Advanced Proteomics Techniques and Applications (2 papers), Advanced Data Storage Technologies (2 papers) and Machine Learning and Data Classification (2 papers). The work is most often cited by research in Molecular Biology (376 citations), Computer Science Applications (26 citations), Health Informatics (6 citations), Artificial Intelligence (83 citations) and Microbiology (16 citations). Cam Macdonell has collaborated with scholars based in Canada, United States and South Sudan. Frequent co-authors include David S. Wishart, P. Lu, Roman Eisner, Duane Szafron, Russell Greiner, Brett Poulin, John Anvik, Zhonghua Lu, Alona Fyshe and You Zhou. Their work appears in journals such as Nucleic Acids Research, Bioinformatics, Journal of Parallel and Distributed Computing, University of Alberta Library 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.

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