Gordon M. Shepherd

2.1k total citations
30 papers, 670 citations indexed

About

Gordon M. Shepherd is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems and Management. According to data from OpenAlex, Gordon M. Shepherd has authored 30 papers receiving a total of 670 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 17 papers in Molecular Biology and 3 papers in Information Systems and Management. Recurrent topics in Gordon M. Shepherd's work include Biomedical Text Mining and Ontologies (16 papers), Semantic Web and Ontologies (12 papers) and Bioinformatics and Genomic Networks (6 papers). Gordon M. Shepherd is often cited by papers focused on Biomedical Text Mining and Ontologies (16 papers), Semantic Web and Ontologies (12 papers) and Bioinformatics and Genomic Networks (6 papers). Gordon M. Shepherd collaborates with scholars based in United States, Austria and Hungary. Gordon M. Shepherd's 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 and has published in prestigious journals such as Journal of Neuroscience, Brain Research Reviews and BMC Bioinformatics.

In The Last Decade

Gordon M. Shepherd

29 papers receiving 635 citations

Peers

Gordon M. Shepherd
Comparison fields: 5 of 105
  • Molecular Biology 380
  • Artificial Intelligence 257
  • Cognitive Neuroscience 111
  • Information Systems and Management 102
  • Biophysics 78
Replace Emmanouil Skoufos with:
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Emmanouil Skoufos United States View profile →
Citations per field, relative to Gordon M. Shepherd
Gordon M. Shepherd · 1×
Citations per year, relative to Gordon M. Shepherd
Gordon M. Shepherd · 1×

Countries citing papers authored by Gordon M. Shepherd

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
# 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

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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