Michael Fernández
- Molecular Biology top 2%
- Computational Theory and Mathematics top 0.2%
- Materials Chemistry top 5%
- Microbiology top 0.5%
- Organic Chemistry top 5%
- Co-authors
- Julio CaballeroTom K. WooAmanda S. BarnardArtem CherkasovLeyden FernándezThomas D. DaffPeter G. BoydMohammad Zein Aghaji
- Topics
- Computational Drug Discovery Methods (39 papers)Machine Learning in Materials Science (15 papers)Protein Structure and Dynamics (13 papers)
- Journals
- CellNucleic Acids ResearchNeuron
- Partner nations
- CubaUnited StatesChile
In The Last Decade
Michael Fernández
121 papers receiving 5.3k citations
Hit Papers
Peers
Comparison fields: 5 of 182
- Molecular Biology 2.7k
- Computational Theory and Mathematics 1.3k
- Materials Chemistry 1.2k
- Microbiology 644
- Organic Chemistry 574
Countries citing papers authored by Michael Fernández
This map shows the geographic impact of Michael Fernández'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 Michael Fernández with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Fernández more than expected).
Fields of papers citing papers by Michael Fernández
This network shows the impact of papers produced by Michael Fernández. 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 Michael Fernández. The network helps show where Michael Fernández may publish in the future.
Co-authorship network of co-authors of Michael Fernández
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Fernández. A scholar is included among the top collaborators of Michael Fernández 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 Michael Fernández. Michael Fernández is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 13 | |
| 2 | 2 | |
| 3 | 7 | |
| 4 | 19 | |
| 5 | 3 | |
| 6 | 93 | |
| 7 | 14 | |
| 8 | A Computational Assessment of the Robustness of Cancer Treatments with Respect to Immune Response Strength, Tumor Size and Resistance | 6 |
| 9 | 25 | |
| 10 | 29 | |
| 11 | 22 | |
| 12 | 39 | |
| 13 | 77 | |
| 14 | 43 | |
| 15 | 8 | |
| 16 | 39 | |
| 17 | 34 | |
| 18 | 75 | |
| 19 | 54 | |
| 20 | 17 |
About Michael Fernández
Michael Fernández is a scholar working on Computational Theory and Mathematics, Biotechnology and Hematology, having authored 123 papers that have together received 5.4k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (39 papers), Machine Learning in Materials Science (15 papers) and Protein Structure and Dynamics (13 papers). The work is most often cited by research in Microbiology (644 citations), Computational Theory and Mathematics (1.3k citations) and Inorganic Chemistry (561 citations). Michael Fernández has collaborated with scholars based in Cuba, United States and Chile. Frequent co-authors include Julio Caballero, Tom K. Woo, Amanda S. Barnard, Artem Cherkasov, Leyden Fernández, Thomas D. Daff, Peter G. Boyd, Mohammad Zein Aghaji, Alain Tundidor‐Camba and Reynaldo Villalonga. Their work appears in journals such as Cell, Nucleic Acids Research and Neuron.
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.