Paul E. Goss

16.7k total citations · 4 hit papers
214 papers, 10.6k citations indexed

About

Paul E. Goss is a scholar working on Oncology, Genetics and Cancer Research. According to data from OpenAlex, Paul E. Goss has authored 214 papers receiving a total of 10.6k indexed citations (citations by other indexed papers that have themselves been cited), including 128 papers in Oncology, 111 papers in Genetics and 91 papers in Cancer Research. Recurrent topics in Paul E. Goss's work include Estrogen and related hormone effects (94 papers), Breast Cancer Treatment Studies (79 papers) and Advanced Breast Cancer Therapies (42 papers). Paul E. Goss is often cited by papers focused on Estrogen and related hormone effects (94 papers), Breast Cancer Treatment Studies (79 papers) and Advanced Breast Cancer Therapies (42 papers). Paul E. Goss collaborates with scholars based in United States, Canada and United Kingdom. Paul E. Goss's co-authors include Kathrin Strasser‐Weippl, Beverly Moy, Dianne M. Finkelstein, James N. Ingle, Lei Fan, Jessica St. Louis, Dongsheng Tu, Kathleen I. Pritchard, Zhi‐Ming Shao and Ann F. Chambers and has published in prestigious journals such as New England Journal of Medicine, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Paul E. Goss

208 papers receiving 10.3k citations

Hit Papers

Breast cancer in China 2005 2026 2012 2019 2014 2005 2011 2016 250 500 750 1000

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Paul E. Goss United States 47 5.4k 3.8k 3.7k 2.3k 2.1k 214 10.6k
Ping Sun Canada 47 4.9k 0.9× 4.4k 1.2× 4.2k 1.2× 2.8k 1.3× 1.2k 0.6× 128 10.9k
Vered Stearns United States 57 6.6k 1.2× 4.3k 1.1× 4.6k 1.2× 2.9k 1.3× 2.1k 1.0× 327 14.0k
Christopher I. Li United States 55 5.9k 1.1× 3.8k 1.0× 2.1k 0.6× 1.8k 0.8× 1.5k 0.7× 196 10.2k
Banu Arun United States 52 6.1k 1.1× 3.9k 1.0× 3.5k 0.9× 3.3k 1.5× 1.7k 0.8× 260 11.5k
Leslie G. Ford United States 32 4.3k 0.8× 3.5k 0.9× 4.8k 1.3× 2.6k 1.2× 3.7k 1.8× 74 13.2k
Robert Paridaens Belgium 55 6.3k 1.2× 3.6k 1.0× 3.3k 0.9× 1.8k 0.8× 2.1k 1.0× 307 9.9k
Timothy R. Rebbeck United States 56 3.7k 0.7× 2.7k 0.7× 5.6k 1.5× 3.0k 1.3× 2.0k 1.0× 257 12.7k
Richard Elledge United States 48 6.2k 1.1× 4.3k 1.1× 2.5k 0.7× 2.8k 1.2× 1.9k 0.9× 102 10.0k
Kathleen E. Malone United States 63 7.2k 1.3× 4.0k 1.1× 3.9k 1.1× 2.7k 1.2× 1.2k 0.6× 221 12.3k
Michael Baum United Kingdom 53 6.3k 1.2× 5.8k 1.5× 3.5k 1.0× 2.2k 1.0× 2.5k 1.2× 283 13.3k

Countries citing papers authored by Paul E. Goss

Since Specialization
Citations

This map shows the geographic impact of Paul E. Goss'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 Paul E. Goss with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul E. Goss more than expected).

Fields of papers citing papers by Paul E. Goss

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Paul E. Goss. 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 Paul E. Goss. The network helps show where Paul E. Goss may publish in the future.

Co-authorship network of co-authors of Paul E. Goss

This figure shows the co-authorship network connecting the top 25 collaborators of Paul E. Goss. A scholar is included among the top collaborators of Paul E. Goss 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 Paul E. Goss. Paul E. Goss is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Jindal, Sonali, Nathan D. Pennock, Duanchen Sun, et al.. (2021). Postpartum breast cancer has a distinct molecular profile that predicts poor outcomes. Nature Communications. 12(1). 6341–6341. 21 indexed citations
3.
Cairns, Junmei, James N. Ingle, Tanda M. Dudenkov, et al.. (2020). Pharmacogenomics of aromatase inhibitors in postmenopausal breast cancer and additional mechanisms of anastrozole action. JCI Insight. 5(16). 22 indexed citations
4.
Bukowski, Alexandra, et al.. (2020). Prediction of Attendance to the "Law of 60 Days" in Breast Cancer Patients using Machine Learning Classifiers. 4(3). 1–11. 2 indexed citations
5.
Ingle, James N., Fang Xie, Matthew J. Ellis, et al.. (2016). Genetic Polymorphisms in the Long Noncoding RNA MIR2052HG Offer a Pharmacogenomic Basis for the Response of Breast Cancer Patients to Aromatase Inhibitor Therapy. Cancer Research. 76(23). 7012–7023. 43 indexed citations
6.
Chapman, Judith‐Anne W., Dennis C. Sgroi, Paul E. Goss, et al.. (2016). Relapse-free survival of statistically standardized continuous RT-PCR estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2): NCIC CTG MA.14. Breast Cancer Research and Treatment. 157(1). 101–108.
8.
Liedke, Pedro Emanuel Rubini, Dianne M. Finkelstein, Jackie Szymonifka, et al.. (2013). Outcomes of Breast Cancer in Brazil Related to Health Care Coverage: A Retrospective Cohort Study. Cancer Epidemiology Biomarkers & Prevention. 23(1). 126–133. 71 indexed citations
9.
Strasser‐Weippl, Kathrin & Paul E. Goss. (2013). Competing Risks in Low-Risk Breast Cancer. American Society of Clinical Oncology Educational Book. 33. 32–39. 4 indexed citations
10.
Sgroi, Dennis C., Erin Carney, Elizabeth Zarrella, et al.. (2013). Prediction of Late Disease Recurrence and Extended Adjuvant Letrozole Benefit by the HOXB13/IL17BR Biomarker. JNCI Journal of the National Cancer Institute. 105(14). 1036–1042. 153 indexed citations
11.
Goss, Paul E., Ian E. Smith, Joyce O’Shaughnessy, et al.. (2012). Adjuvant lapatinib for women with early-stage HER2-positive breast cancer: a randomised, controlled, phase 3 trial. The Lancet Oncology. 14(1). 88–96. 100 indexed citations
13.
Cigler, Tessa, Dongsheng Tu, Martin J. Yaffe, et al.. (2009). A randomized, placebo-controlled trial (NCIC CTG MAP1) examining the effects of letrozole on mammographic breast density and other end organs in postmenopausal women. Breast Cancer Research and Treatment. 120(2). 427–435. 30 indexed citations
14.
Moy, Beverly, Peter Kirkpatrick, Santwana Kar, & Paul E. Goss. (2007). Lapatinib. Nature Reviews Drug Discovery. 6(6). 431–432. 127 indexed citations
15.
Goss, Paul E. & Harriet Richardson. (2007). The NCIC CTG MAP.3 Trial: An international breast cancer prevention trial. Current Oncology. 14(3). 89–95. 3 indexed citations
16.
Goss, Paul E. & Melinda Wu. (2007). Application of aromatase inhibitors in endocrine responsive breast cancers. The Breast. 16. 114–119. 9 indexed citations
17.
Moy, Beverly & Paul E. Goss. (2007). Lapatinib-Associated Toxicity and Practical Management Recommendations. The Oncologist. 12(7). 756–765. 87 indexed citations
18.
Goss, Paul E., Harriet Richardson, Rowan T. Chlebowski, et al.. (2007). National Cancer Institute of Canada Clinical Trials Group MAP.3 Trial: Evaluation of Exemestane to Prevent Breast Cancer in Postmenopausal Women. Clinical Breast Cancer. 7(11). 895–900. 11 indexed citations
19.
Hack, Thomas F., Lorenzo Cohen, Joel Katz, Lynda S. Robson, & Paul E. Goss. (1999). Physical and Psychological Morbidity After Axillary Lymph Node Dissection for Breast Cancer. Journal of Clinical Oncology. 17(1). 143–143. 304 indexed citations
20.
Goss, Paul E., et al.. (1994). Current perspectives on aromatase inhibitors in breast cancer.. Journal of Clinical Oncology. 12(11). 2460–2470. 54 indexed citations

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.

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