Steve Goodison
- Cancer Research top 1%
- Protease and Inhibitor Mechanisms 10
- Oncology top 2%
- Molecular Biology top 2%
- Gene expression and cancer classification 21
- Bioinformatics and Genomic Networks 16
- Glycosylation and Glycoproteins Research 12
- Angiogenesis and VEGF in Cancer 11
- Immunology and Allergy top 2%
- Immunology top 5%
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- Bladder and Urothelial Cancer Treatments 36
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- Advanced Proteomics Techniques and Applications 18
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- Prostate Cancer Treatment and Research 11
- Co-authors
- Virginia UrquidiCharles J. RosserYijun SunMakito MiyakeDavid G. JacksonSiniša TodorovićYunfeng DaiTakashi Sugino
- Journals
- The Journal of Urology (9 papers)Cancer Epidemiology Biomarkers & Prevention (4 papers)BMC Cancer (4 papers)
- Partner nations
- United StatesJapanUnited Kingdom
In The Last Decade
Steve Goodison
157 papers receiving 6.0k citations
Hit Papers
Peers
Comparison fields: 5 of 165
- Cancer Research 1.2k
- Oncology 1.5k
- Molecular Biology 3.2k
- Immunology and Allergy 266
- Immunology 724
Countries citing papers authored by Steve Goodison
This map shows the geographic impact of Steve Goodison'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 Steve Goodison with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steve Goodison more than expected).
Fields of papers citing papers by Steve Goodison
This network shows the impact of papers produced by Steve Goodison. 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 Steve Goodison. The network helps show where Steve Goodison may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Steve Goodison, 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 | 2023 | 4 | |
| 2 | 2022 | 3 | |
| 3 | 2022 | 3 | |
| 4 | 2019 | 92 | |
| 5 | 2019 | 12 | |
| 6 | 2017 | 13 | |
| 7 | 2016 | 7 | |
| 8 | 2015 | 13 | |
| 9 | 2014 | 45 | |
| 10 | 2014 | 75 | |
| 11 | 2013 | 61 | |
| 12 | 2013 | 1 | |
| 13 | 2013 | 110 | |
| 14 | 2012 | 72 | |
| 15 | 2012 | 55 | |
| 16 | 2009 | 97 | |
| 17 | Predicting Breast Cancer Metastasis by Integrating Both Clinical and Genetic Markers. | 2007 | 1 |
| 18 | 2007 | 17 | |
| 19 | 2002 | 41 | |
| 20 | 2001 | 54 |
About Steve Goodison
Steve Goodison is a scholar working on Cancer Research, Molecular Biology and Immunology and Allergy, having authored 159 papers that have together received 6.1k indexed citations. Recurring topics across this work include Bladder and Urothelial Cancer Treatments (36 papers), Gene expression and cancer classification (21 papers), Advanced Proteomics Techniques and Applications (18 papers), Bioinformatics and Genomic Networks (16 papers), Glycosylation and Glycoproteins Research (12 papers), Angiogenesis and VEGF in Cancer (11 papers), Prostate Cancer Treatment and Research (11 papers) and Protease and Inhibitor Mechanisms (10 papers). The work is most often cited by research in Cancer Research (1.2k citations), Oncology (1.5k citations) and Molecular Biology (3.2k citations). Steve Goodison has collaborated with scholars based in United States, Japan and United Kingdom. Frequent co-authors include Virginia Urquidi, Charles J. Rosser, Yijun Sun, Makito Miyake, David G. Jackson, Siniša Todorović, Yunfeng Dai, Yijun Sun, Takashi Sugino and Myron Chang. Their work appears in journals such as The Journal of Urology, Cancer Epidemiology Biomarkers & Prevention, BMC Cancer, Journal of Translational Medicine and Biomarkers in Medicine.
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.