Gordon M. Shepherd
- Molecular Biology
- Artificial Intelligence top 5%
- Cognitive Neuroscience top 10%
- Information Systems and Management top 5%
- Biophysics top 5%
- Co-authors
- Perry L. MillerLuis MarencoEmmanouil SkoufosRunsheng ChenMaryann E. MartoneP. NadkarniGiorgio A. AscoliPrakash M. Nadkarni
- Topics
- Biomedical Text Mining and Ontologies (16 papers)Semantic Web and Ontologies (12 papers)Bioinformatics and Genomic Networks (6 papers)
- Partner nations
- United StatesAustriaHungary
In The Last Decade
Gordon M. Shepherd
29 papers receiving 635 citations
Peers
Comparison fields: 5 of 105
- Molecular Biology 380
- Artificial Intelligence 257
- Cognitive Neuroscience 111
- Information Systems and Management 102
- Biophysics 78
Countries citing papers authored by Gordon M. Shepherd
This map shows the geographic impact of Gordon M. Shepherd'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 Gordon M. Shepherd with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gordon M. Shepherd more than expected).
Fields of papers citing papers by Gordon M. Shepherd
This network shows the impact of papers produced by Gordon M. Shepherd. 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 Gordon M. Shepherd. The network helps show where Gordon M. Shepherd may publish in the future.
Co-authorship network of co-authors of Gordon M. Shepherd
This figure shows the co-authorship network connecting the top 25 collaborators of Gordon M. Shepherd. A scholar is included among the top collaborators of Gordon M. Shepherd 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 Gordon M. Shepherd. Gordon M. Shepherd is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 20 | |
| 3 | 12 | |
| 4 | 30 | |
| 5 | 14 | |
| 6 | 6 | |
| 7 | 4 | |
| 8 | 107 | |
| 9 | 13 | |
| 10 | 50 | |
| 11 | 4 | |
| 12 | 1 | |
| 13 | 36 | |
| 14 | 14 | |
| 15 | 22 | |
| 16 | 41 | |
| 17 | 15 | |
| 18 | 145 | |
| 19 | 27 | |
| 20 | The significance of real neuron architectures for neural network simulations | 16 |
About Gordon M. Shepherd
Gordon M. Shepherd is a scholar working on Artificial Intelligence, Information Systems and Management and Sensory Systems, having authored 30 papers that have together received 670 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (16 papers), Semantic Web and Ontologies (12 papers) and Bioinformatics and Genomic Networks (6 papers). The work is most often cited by research in Information Systems and Management (102 citations), Biophysics (78 citations) and Sensory Systems (52 citations). Gordon M. Shepherd has collaborated with scholars based in United States, Austria and Hungary. Frequent co-authors include Perry L. Miller, Luis Marenco, Emmanouil Skoufos, Runsheng Chen, Maryann E. Martone, P. Nadkarni, Giorgio A. Ascoli, Prakash M. Nadkarni, Jeffrey S. Grethe and Chiquito Crasto. Their work appears in journals such as Journal of Neuroscience, Brain Research Reviews and BMC Bioinformatics.
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.