Susumu Goto
- Molecular Biology top 0.02%
- Microbial Metabolic Engineering and Bioproduction 41
- Bioinformatics and Genomic Networks 40
- Genomics and Phylogenetic Studies 35
- Metabolomics and Mass Spectrometry Studies 20
- Machine Learning in Bioinformatics 15
- RNA and protein synthesis mechanisms 13
- Cancer Research top 0.1%
- Computational Theory and Mathematics top 0.05%
- Computational Drug Discovery Methods 27
- Ecology top 0.1%
- Bacteriophages and microbial interactions 10
- Aging top 0.5%
Susumu Goto
139 papers receiving 46.5k citations
Hit Papers
Peers
Comparison fields: 5 of 207
- Molecular Biology 31.5k
- Cancer Research 4.9k
- Computational Theory and Mathematics 3.6k
- Ecology 5.0k
- Aging 295
Countries citing papers authored by Susumu Goto
This map shows the geographic impact of Susumu Goto'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 Susumu Goto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Susumu Goto more than expected).
Fields of papers citing papers by Susumu Goto
This network shows the impact of papers produced by Susumu Goto. 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 Susumu Goto. The network helps show where Susumu Goto may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Susumu Goto, 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 | 1 | |
| 3 | 2024 | 15 | |
| 4 | 2022 | 6 | |
| 5 | KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score thresholdbreakdown → | 2019 | 1159 |
| 6 | 2018 | 32 | |
| 7 | 2017 | 46 | |
| 8 | 2016 | 91 | |
| 9 | KEGG for integration and interpretation of large-scale molecular data setsbreakdown → | 2011 | 3753 |
| 10 | 2009 | 69 | |
| 11 | 2009 | 75 | |
| 12 | 2007 | 160 | |
| 13 | 2003 | 7 | |
| 14 | 2003 | 2 | |
| 15 | 2003 | 4 | |
| 16 | 2003 | 1 | |
| 17 | 2002 | 2 | |
| 18 | 2002 | 2 | |
| 19 | KEGG: Kyoto Encyclopedia of Genes and Genomesbreakdown → | 1999 | 25542 |
| 20 | 1997 | 1 |
About Susumu Goto
Susumu Goto is a scholar working on Molecular Biology, Computational Theory and Mathematics and Pharmacology, having authored 143 papers that have together received 47.1k indexed citations. Recurring topics across this work include Microbial Metabolic Engineering and Bioproduction (41 papers), Bioinformatics and Genomic Networks (40 papers), Genomics and Phylogenetic Studies (35 papers), Computational Drug Discovery Methods (27 papers), Metabolomics and Mass Spectrometry Studies (20 papers), Machine Learning in Bioinformatics (15 papers), RNA and protein synthesis mechanisms (13 papers) and Bacteriophages and microbial interactions (10 papers). The work is most often cited by research in Molecular Biology (31.5k citations), Cancer Research (4.9k citations) and Computational Theory and Mathematics (3.6k citations). Susumu Goto has collaborated with scholars based in Japan, Spain and Sweden. Frequent co-authors include Minoru Kanehisa, Hiroyuki Ogata, Wataru Fujibuchi, Hidemasa Bono, Miho Furumichi, Yoko Sato, Mika Hirakawa, Mao Tanabe, Mikio Tanabe and Masahiro Hattori. Their work appears in journals such as Journal of the American Chemical Society, Nucleic Acids Research and Environmental Science & Technology.
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.