Richard Hughey
- Molecular Biology top 2%
- Materials Chemistry top 10%
- Artificial Intelligence top 2%
- Genetics top 5%
- Plant Science top 10%
- Co-authors
- Kevin KarplusCurtis L. BarrettCyrus ChothiaAnders KroghJulian GoughDavid HausslerChristian BarrettShahzad I. Mian
- Topics
- Algorithms and Data Compression (20 papers)Machine Learning in Bioinformatics (15 papers)Genomics and Phylogenetic Studies (14 papers)
- Partner nations
- United StatesUnited KingdomChile
In The Last Decade
Richard Hughey
58 papers receiving 4.5k citations
Hit Papers
Peers
Comparison fields: 5 of 146
- Molecular Biology 3.8k
- Materials Chemistry 711
- Artificial Intelligence 690
- Genetics 347
- Plant Science 332
Countries citing papers authored by Richard Hughey
This map shows the geographic impact of Richard Hughey'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 Richard Hughey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Hughey more than expected).
Fields of papers citing papers by Richard Hughey
This network shows the impact of papers produced by Richard Hughey. 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 Richard Hughey. The network helps show where Richard Hughey may publish in the future.
Co-authorship network of co-authors of Richard Hughey
This figure shows the co-authorship network connecting the top 25 collaborators of Richard Hughey. A scholar is included among the top collaborators of Richard Hughey 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 Richard Hughey. Richard Hughey is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 32 | |
| 2 | 235 | |
| 3 | 63 | |
| 4 | 20 | |
| 5 | Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structurebreakdown → | 962 |
| 6 | 102 | |
| 7 | The UCSC Kestrel General Purpose Parallel Processor. | 20 |
| 8 | 90 | |
| 9 | Hidden Markov models for detecting remote protein homologies.breakdown → | 861 |
| 10 | 423 | |
| 11 | 32 | |
| 12 | 20 | |
| 13 | 65 | |
| 14 | 67 | |
| 15 | 5 | |
| 16 | 33 | |
| 17 | 53 | |
| 18 | 246 | |
| 19 | 248 | |
| 20 | B-SYS: A 470-Processor Programmable Systolic Array | 19 |
About Richard Hughey
Richard Hughey is a scholar working on Hardware and Architecture, Artificial Intelligence and Computational Theory and Mathematics, having authored 60 papers that have together received 4.7k indexed citations. Recurring topics across this work include Algorithms and Data Compression (20 papers), Machine Learning in Bioinformatics (15 papers) and Genomics and Phylogenetic Studies (14 papers). The work is most often cited by research in Molecular Biology (3.8k citations), Artificial Intelligence (690 citations) and Hardware and Architecture (126 citations). Richard Hughey has collaborated with scholars based in United States, United Kingdom and Chile. Frequent co-authors include Kevin Karplus, Curtis L. Barrett, Cyrus Chothia, Anders Krogh, Julian Gough, David Haussler, Christian Barrett, Shahzad I. Mian, Kimmen Sjölander and Mark Diekhans. Their work appears in journals such as Nucleic Acids Research, Bioinformatics and Journal of Molecular Biology.
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.