Matthew M. Gubin
- Immunology top 0.5%
- Immunotherapy and Immune Responses 9
- Immune Cell Function and Interaction 7
- Oncology top 0.5%
- Cancer Immunotherapy and Biomarkers 10
- CAR-T cell therapy research 3
- Cancer Research top 1%
- Molecular Biology top 5%
- RNA Research and Splicing 8
- RNA modifications and cancer 6
- RNA and protein synthesis mechanisms 6
- vaccines and immunoinformatics approaches 2
- Biological Psychiatry top 10%
- Co-authors
- Robert D. SchreiberMark J. SmythDeepak MittalTakuro NoguchiChih‐Hao ChangMichael D. BuckErika L. PearceGerritje J. W. van der Windt
- Cited by
- ImmunologyOncologyCancer Research
- Partner nations
- United StatesSingaporeAustralia
In The Last Decade
Matthew M. Gubin
22 papers receiving 5.0k citations
Hit Papers
Peers
Comparison fields: 5 of 110
- Immunology 2.8k
- Oncology 2.7k
- Cancer Research 1.2k
- Molecular Biology 1.7k
- Biological Psychiatry 46
Countries citing papers authored by Matthew M. Gubin
This map shows the geographic impact of Matthew M. Gubin'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 Matthew M. Gubin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew M. Gubin more than expected).
Fields of papers citing papers by Matthew M. Gubin
This network shows the impact of papers produced by Matthew M. Gubin. 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 Matthew M. Gubin. The network helps show where Matthew M. Gubin may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Matthew M. Gubin, 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 | 2022 | 75 | |
| 3 | 2022 | 24 | |
| 4 | 2018 | 275 | |
| 5 | 2017 | 242 | |
| 6 | 2017 | 91 | |
| 7 | 2017 | 3 | |
| 8 | 2016 | 178 | |
| 9 | 2016 | 3 | |
| 10 | 2015 | 12 | |
| 11 | Metabolic Competition in the Tumor Microenvironment Is a Driver of Cancer Progressionbreakdown → | 2015 | 2297 |
| 12 | Tumor neoantigens: building a framework for personalized cancer immunotherapybreakdown → | 2015 | 465 |
| 13 | 2014 | 32 | |
| 14 | New insights into cancer immunoediting and its three component phases — elimination, equilibrium and escapebreakdown → | 2014 | 1078 |
| 15 | 2013 | 70 | |
| 16 | 2012 | 26 | |
| 17 | 2012 | 17 | |
| 18 | 2012 | 4 | |
| 19 | 2010 | 51 | |
| 20 | 2010 | 50 |
About Matthew M. Gubin
Matthew M. Gubin is a scholar working on Immunology, Oncology and Cancer Research, having authored 22 papers that have together received 5.1k indexed citations. Recurring topics across this work include Cancer Immunotherapy and Biomarkers (10 papers), Immunotherapy and Immune Responses (9 papers), RNA Research and Splicing (8 papers), Immune Cell Function and Interaction (7 papers), RNA modifications and cancer (6 papers), RNA and protein synthesis mechanisms (6 papers), CAR-T cell therapy research (3 papers) and vaccines and immunoinformatics approaches (2 papers). The work is most often cited by research in Immunology (2.8k citations), Oncology (2.7k citations) and Cancer Research (1.2k citations). Matthew M. Gubin has collaborated with scholars based in United States, Singapore and Australia. Frequent co-authors include Robert D. Schreiber, Mark J. Smyth, Deepak Mittal, Takuro Noguchi, Chih‐Hao Chang, Michael D. Buck, Erika L. Pearce, Gerritje J. W. van der Windt, Qiongyu Chen and David O’Sullivan.
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.