M. Michael Gromiha
- Molecular Biology top 0.5%
- Protein Structure and Dynamics 187
- RNA and protein synthesis mechanisms 134
- Machine Learning in Bioinformatics 85
- Bioinformatics and Genomic Networks 23
- Glycosylation and Glycoproteins Research 20
- DNA and Nucleic Acid Chemistry 15
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods 40
- Materials Chemistry top 2%
- Enzyme Structure and Function 93
- Spectroscopy top 2%
- Biotechnology top 2%
- Co-authors
- S. SelvarajShandar AhmadAkinori SaraiDietmar SchomburgVijaya ParthibanMakiko SuwaGajendra P. S. RaghavaManish Kumar
- Partner nations
- IndiaJapanUnited States
In The Last Decade
M. Michael Gromiha
328 papers receiving 9.8k citations
Hit Papers
Peers
Comparison fields: 5 of 174
- Molecular Biology 8.4k
- Computational Theory and Mathematics 1.1k
- Materials Chemistry 2.1k
- Spectroscopy 482
- Biotechnology 251
Countries citing papers authored by M. Michael Gromiha
This map shows the geographic impact of M. Michael Gromiha'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 M. Michael Gromiha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Michael Gromiha more than expected).
Fields of papers citing papers by M. Michael Gromiha
This network shows the impact of papers produced by M. Michael Gromiha. 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 M. Michael Gromiha. The network helps show where M. Michael Gromiha may publish in the future.
Co-authorship network
The 25 scholars most cited alongside M. Michael Gromiha, 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 | 2024 | 6 | |
| 2 | 2024 | 4 | |
| 3 | 2024 | 6 | |
| 4 | 2024 | 2 | |
| 5 | 2023 | 2 | |
| 6 | 2023 | 8 | |
| 7 | 2023 | 6 | |
| 8 | 2023 | 1 | |
| 9 | 2022 | 3 | |
| 10 | 2022 | 16 | |
| 11 | 2021 | 4 | |
| 12 | 2021 | 16 | |
| 13 | 2020 | 4 | |
| 14 | 2020 | 148 | |
| 15 | 2019 | 31 | |
| 16 | Intelligent Computing in Bioinformatics: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014, Proceedings | 2014 | 3 |
| 17 | 2009 | 31 | |
| 18 | 2004 | 102 | |
| 19 | Recent research developments in protein folding stability & design | 2002 | 3 |
| 20 | 2002 | 2 |
About M. Michael Gromiha
M. Michael Gromiha is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry, having authored 339 papers that have together received 10.0k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (187 papers), RNA and protein synthesis mechanisms (134 papers), Enzyme Structure and Function (93 papers), Machine Learning in Bioinformatics (85 papers), Computational Drug Discovery Methods (40 papers), Bioinformatics and Genomic Networks (23 papers), Glycosylation and Glycoproteins Research (20 papers) and DNA and Nucleic Acid Chemistry (15 papers). The work is most often cited by research in Molecular Biology (8.4k citations), Computational Theory and Mathematics (1.1k citations) and Materials Chemistry (2.1k citations). M. Michael Gromiha has collaborated with scholars based in India, Japan and United States. Frequent co-authors include S. Selvaraj, Shandar Ahmad, Akinori Sarai, Dietmar Schomburg, Vijaya Parthiban, Makiko Suwa, Gajendra P. S. Raghava, Manish Kumar, P. K. Ponnuswamy and Motohisa Oobatake.
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.