Michael Terribilini
Impact in
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- RNA and protein synthesis mechanisms
- RNA Research and Splicing
- Protein Structure and Dynamics
- RNA modifications and cancer
- Machine Learning in Bioinformatics
- Genomics and Phylogenetic Studies
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- Cancer-related molecular mechanisms research
Papers in
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- RNA and protein synthesis mechanisms 6
- RNA Research and Splicing 4
- Machine Learning in Bioinformatics 3
- Protein Structure and Dynamics 3
- RNA modifications and cancer 2
- Genetics, Bioinformatics, and Biomedical Research 1
- Genomics and Phylogenetic Studies 1
- Co-authors
- Drena Dobbs (7 shared papers)Vasant Honavar (7 shared papers)Robert L. Jernigan (4 shared papers)Jae‐Hyung Lee (4 shared papers)Changhui Yan (3 shared papers)Rasna R. Walia (2 shared papers)Jeffry D. Sander (2 shared papers)Feihong Wu (2 shared papers)
- Journals
- Nucleic Acids Research (2 papers)BMC Bioinformatics (2 papers)Applied Biochemistry and Biotechnology (1 paper)RNA (1 paper)PubMed (2 papers)
- Partner nations
- United StatesEgypt
In The Last Decade
Michael Terribilini
8 papers receiving 611 citations
Peers
Comparison fields: 5 of 41
- Molecular Biology 577
- Cancer Research 44
- Computational Theory and Mathematics 44
- Animal Science and Zoology 8
- Genetics 21
Countries citing papers authored by Michael Terribilini
This map shows the geographic impact of Michael Terribilini'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 Terribilini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Terribilini more than expected).
Fields of papers citing papers by Michael Terribilini
This network shows the impact of papers produced by Michael Terribilini. 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 Terribilini. The network helps show where Michael Terribilini may publish in the future.
Co-authors
The 21 scholars most cited alongside Michael Terribilini, 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 | 2007 | 153 | |
| 2 | 2006 | 134 | |
| 3 | 2006 | 121 | |
| 4 | 2010 | 107 | |
| 5 | 2012 | 74 | |
| 6 | 2007 | 12 | |
| 7 | 2005 | 8 | |
| 8 | 2007 | 5 |
About Michael Terribilini
Michael Terribilini is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine, Biotechnology, Food Science and Materials Chemistry, having authored 8 papers that have together received 614 indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (6 papers), RNA Research and Splicing (4 papers), Machine Learning in Bioinformatics (3 papers), Protein Structure and Dynamics (3 papers), RNA modifications and cancer (2 papers), Genetics, Bioinformatics, and Biomedical Research (1 paper), Enzyme Structure and Function (1 paper) and Genomics and Phylogenetic Studies (1 paper). The work is most often cited by research in Molecular Biology (577 citations), Cancer Research (44 citations), Computational Theory and Mathematics (44 citations), Animal Science and Zoology (8 citations) and Genetics (21 citations). Michael Terribilini has collaborated with scholars based in United States and Egypt. Frequent co-authors include Drena Dobbs, Vasant Honavar, Robert L. Jernigan, Jae‐Hyung Lee, Changhui Yan, Rasna R. Walia, Jeffry D. Sander, Feihong Wu, Charles Zheng and Jane F. Ferguson. Their work appears in journals such as Nucleic Acids Research, BMC Bioinformatics, Applied Biochemistry and Biotechnology, RNA and PubMed.
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.