Alexander Derry

1.3k citations
9 papers · 412 · h-index 7

Impact in

Papers in

Alexander Derry

9 papers receiving 408 citations

Peers

Alexander Derry
Comparison fields: 5 of 98
  • Cellular and Molecular Neuroscience 93
  • Health Informatics 5
  • Polymers and Plastics 50
  • Computational Theory and Mathematics 50
  • Biomedical Engineering 133
Replace Sunghoon Joo with:
Sunghoon Joo South Korea
Fumiaki Tanaka Japan
Jeongyeon Seo South Korea
Zuwan Lin United States
Bige Deniz Unluturk United States
Natalie Dowell‐Mesfin United States
Gal Chen Israel
Yu Wu China
Ryo Ikeda Japan
Alexander Derry relative to Sunghoon Joo South Korea Sunghoon Joo's profile →
Citations per field
00.5×1.5×2.4×
Sunghoon Joo · 1×
Citations per year

Countries citing papers authored by Alexander Derry

Since Specialization
Citations

This map shows the geographic impact of Alexander Derry'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 Alexander Derry with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander Derry more than expected).

Fields of papers citing papers by Alexander Derry

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alexander Derry. 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 Alexander Derry. The network helps show where Alexander Derry may publish in the future.

Co-authors

The 19 scholars most cited alongside Alexander Derry, 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 Alexander Derry Line = papers co-authored together Alexander Derry links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1 2017191
2 202292
3 201954
4 202350
5 20239
6 20226
7
Training data composition affects performance of protein structure analysis algorithms.
20226
8 20212
9 20252

About Alexander Derry

Alexander Derry is a scholar working on Molecular Biology, Materials Chemistry, Signal Processing, Artificial Intelligence and Cellular and Molecular Neuroscience, having authored 9 papers that have together received 412 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (4 papers), Protein Structure and Dynamics (4 papers), Enzyme Structure and Function (3 papers), Time Series Analysis and Forecasting (2 papers), Neural Networks and Applications (2 papers), RNA and protein synthesis mechanisms (2 papers), Neuroscience and Neural Engineering (1 paper) and Computational Drug Discovery Methods (1 paper). The work is most often cited by research in Cellular and Molecular Neuroscience (93 citations), Health Informatics (5 citations), Polymers and Plastics (50 citations), Computational Theory and Mathematics (50 citations) and Biomedical Engineering (133 citations). Alexander Derry has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Russ B. Altman, Naomi Altman, Martin Krzywinski, Chong Hou, Polina Anikeeva, Imogen Brown, Thomas J. Richner, Yoel Fink, Seongjun Park and Chi Lu. Their work appears in journals such as Nature Methods, Science Advances, Protein Science, Proceedings of the National Academy of Sciences and Nature Communications.

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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