Daniel A. Charlebois
- Co-authors
- Gábor BalázsiMads KærnNezar AbdennurDmitry NevozhayD.G. GoodenoughJoseph CohenMariola SzenkStan Matwin
- Topics
- Gene Regulatory Network Analysis (16 papers)AI-based Problem Solving and Planning (11 papers)Evolution and Genetic Dynamics (10 papers)
- Partner nations
- CanadaUnited StatesRussia
In The Last Decade
Daniel A. Charlebois
33 papers receiving 371 citations
Peers
Comparison fields: 5 of 76
- Molecular Biology 267
- Genetics 107
- Artificial Intelligence 35
- Modeling and Simulation 25
- Cancer Research 24
Countries citing papers authored by Daniel A. Charlebois
This map shows the geographic impact of Daniel A. Charlebois'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 Daniel A. Charlebois with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel A. Charlebois more than expected).
Fields of papers citing papers by Daniel A. Charlebois
This network shows the impact of papers produced by Daniel A. Charlebois. 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 Daniel A. Charlebois. The network helps show where Daniel A. Charlebois may publish in the future.
Co-authorship network of co-authors of Daniel A. Charlebois
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel A. Charlebois. A scholar is included among the top collaborators of Daniel A. Charlebois based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daniel A. Charlebois. Daniel A. Charlebois is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 7 | |
| 3 | 9 | |
| 4 | 5 | |
| 5 | 6 | |
| 6 | 1 | |
| 7 | 67 | |
| 8 | 41 | |
| 9 | 46 | |
| 10 | 4 | |
| 11 | 12 | |
| 12 | 30 | |
| 13 | 5 | |
| 14 | 55 | |
| 15 | Security Classification Using Automated Learning (SCALE): Optimizing Statistical Natural Language Processing Techniques to Assign Security Labels to Unstructured Text | 2 |
| 16 | 2 | |
| 17 | Effects of microarray noise on inference efficiency of a stochastic model of gene networks | 2 |
| 18 | 1 | |
| 19 | 4 | |
| 20 | 1 |
About Daniel A. Charlebois
Daniel A. Charlebois is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing, having authored 38 papers that have together received 378 indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (16 papers), AI-based Problem Solving and Planning (11 papers) and Evolution and Genetic Dynamics (10 papers). The work is most often cited by research in Modeling and Simulation (25 citations), Biophysics (23 citations) and Molecular Biology (267 citations). Daniel A. Charlebois has collaborated with scholars based in Canada, United States and Russia. Frequent co-authors include Gábor Balázsi, Mads Kærn, Nezar Abdennur, Dmitry Nevozhay, D.G. Goodenough, Joseph Cohen, Mariola Szenk, Stan Matwin, Kevin Hauser and Shamanth A. Shankarnarayan. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Physical Review Letters.
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.