Hideo Matsuda
- Molecular Biology top 5%
- Genomics and Phylogenetic Studies 23
- Bioinformatics and Genomic Networks 16
- Machine Learning in Bioinformatics 14
- Gene expression and cancer classification 14
- RNA and protein synthesis mechanisms 11
- Molecular Biology Techniques and Applications 7
- Ecology top 5%
- Genetics top 5%
- Genetic diversity and population structure 8
- Endocrinology top 5%
- Microbiology top 10%
-
- Algorithms and Data Compression 7
- Co-authors
- Gary J. OlsenRoss OverbeekR. HagstromShigeto SenoTakeshi ItohYoji NakamuraTakashi GojoboriYoichi Takenaka
- Cited by
- Molecular BiologyEcologyGenetics
- Journals
- Proceedings Genome Informatics Workshop/Genome informatics (4 papers)Journal of Bioinformatics and Computational Biology (4 papers)Genome Research (4 papers)
- Partner nations
- JapanUnited StatesSouth Korea
In The Last Decade
Hideo Matsuda
122 papers receiving 2.7k citations
Hit Papers
Peers
Comparison fields: 5 of 156
- Molecular Biology 1.6k
- Ecology 550
- Genetics 483
- Endocrinology 73
- Microbiology 10
Countries citing papers authored by Hideo Matsuda
This map shows the geographic impact of Hideo Matsuda'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 Hideo Matsuda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hideo Matsuda more than expected).
Fields of papers citing papers by Hideo Matsuda
This network shows the impact of papers produced by Hideo Matsuda. 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 Hideo Matsuda. The network helps show where Hideo Matsuda may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Hideo Matsuda, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 5 | |
| 4 | 2022 | 2 | |
| 5 | 2021 | 31 | |
| 6 | 2020 | 20 | |
| 7 | 2020 | 12 | |
| 8 | 2017 | 19 | |
| 9 | 2016 | 53 | |
| 10 | 2015 | 98 | |
| 11 | 2013 | 18 | |
| 12 | 2004 | 1 | |
| 13 | Detection of Conserved Domains in Protein Sequences Using a Maximum-Density Subgraph Alogrithm | 2000 | 2 |
| 14 | Hierarchical Approach to Parallel Tree Search for Protein Conformational Analysis. | 2000 | 0 |
| 15 | 1999 | 9 | |
| 16 | 1996 | 4 | |
| 17 | 1995 | 15 | |
| 18 | High Performance I/O System of the Distributed Shared-Memory Massively Parallel Computer JUMP-1 | 1995 | 3 |
| 19 | 1994 | 6 | |
| 20 | 1993 | 0 |
About Hideo Matsuda
Hideo Matsuda is a scholar working on Molecular Biology, Hardware and Architecture, Biophysics, Cancer Research and Software, having authored 139 papers that have together received 2.8k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (23 papers), Bioinformatics and Genomic Networks (16 papers), Machine Learning in Bioinformatics (14 papers), Gene expression and cancer classification (14 papers), RNA and protein synthesis mechanisms (11 papers), Genetic diversity and population structure (8 papers), Algorithms and Data Compression (7 papers) and Molecular Biology Techniques and Applications (7 papers). The work is most often cited by research in Molecular Biology (1.6k citations), Ecology (550 citations), Genetics (483 citations), Endocrinology (73 citations) and Microbiology (10 citations). Hideo Matsuda has collaborated with scholars based in Japan, United States and South Korea. Frequent co-authors include Gary J. Olsen, Ross Overbeek, R. Hagstrom, Shigeto Seno, Takeshi Itoh, Yoji Nakamura, Takashi Gojobori, Yoichi Takenaka, Atsushi Hashimoto and Bei‐Wen Ying. Their work appears in journals such as Proceedings Genome Informatics Workshop/Genome informatics, Journal of Bioinformatics and Computational Biology, Genome Research, Scientific Reports and BMC Genomics.
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.