María C. Marín
- Molecular Biology top 2%
- Oncology top 1%
- Biotechnology top 0.2%
- Cancer Research top 5%
- Genetics top 10%
- Co-authors
- William G. KaelinChristine A. JostMeredith S. IrwinTimothy J. McDonnellKaren H. VousdenLynn S. ChengKeiichi KondoWilliam C. Hahn
- Topics
- Cancer-related Molecular Pathways (24 papers)Cancer Research and Treatments (11 papers)Diabetes, Cardiovascular Risks, and Lipoproteins (7 papers)
- Partner nations
- SpainUnited StatesArgentina
In The Last Decade
María C. Marín
70 papers receiving 4.2k citations
Hit Papers
Peers
Comparison fields: 5 of 115
- Molecular Biology 2.8k
- Oncology 2.6k
- Biotechnology 939
- Cancer Research 552
- Genetics 338
Countries citing papers authored by María C. Marín
This map shows the geographic impact of María C. Marín'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 María C. Marín with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites María C. Marín more than expected).
Fields of papers citing papers by María C. Marín
This network shows the impact of papers produced by María C. Marín. 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 María C. Marín. The network helps show where María C. Marín may publish in the future.
Co-authorship network of co-authors of María C. Marín
This figure shows the co-authorship network connecting the top 25 collaborators of María C. Marín. A scholar is included among the top collaborators of María C. Marín 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 María C. Marín. María C. Marín is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 6 | |
| 4 | 5 | |
| 5 | 33 | |
| 6 | 18 | |
| 7 | 24 | |
| 8 | 52 | |
| 9 | 30 | |
| 10 | 42 | |
| 11 | 37 | |
| 12 | 26 | |
| 13 | 20 | |
| 14 | 5 | |
| 15 | Chemosensitivity linked to p73 functionbreakdown → | 345 |
| 16 | 29 | |
| 17 | 88 | |
| 18 | Role for the p53 homologue p73 in E2F-1-induced apoptosisbreakdown → | 592 |
| 19 | Expression of bcl-2 gene confers multidrug resistance to chemotherapy- induced cell death | 14 |
| 20 | 2 |
About María C. Marín
María C. Marín is a scholar working on Biotechnology, Oncology and Developmental Neuroscience, having authored 71 papers that have together received 4.3k indexed citations. Recurring topics across this work include Cancer-related Molecular Pathways (24 papers), Cancer Research and Treatments (11 papers) and Diabetes, Cardiovascular Risks, and Lipoproteins (7 papers). The work is most often cited by research in Biotechnology (939 citations), Oncology (2.6k citations) and Molecular Biology (2.8k citations). María C. Marín has collaborated with scholars based in Spain, United States and Argentina. Frequent co-authors include William G. Kaelin, Christine A. Jost, Meredith S. Irwin, Timothy J. McDonnell, Karen H. Vousden, Lynn S. Cheng, Keiichi Kondo, William C. Hahn, Elsa R. Flores and Jeremy C. Smith. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.
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.