Tina Seto

642 total citations
26 papers, 435 citations indexed

About

Tina Seto is a scholar working on Oncology, Cancer Research and Molecular Biology. According to data from OpenAlex, Tina Seto has authored 26 papers receiving a total of 435 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Oncology, 6 papers in Cancer Research and 5 papers in Molecular Biology. Recurrent topics in Tina Seto's work include Health Systems, Economic Evaluations, Quality of Life (5 papers), Machine Learning in Healthcare (4 papers) and Breast Cancer Treatment Studies (4 papers). Tina Seto is often cited by papers focused on Health Systems, Economic Evaluations, Quality of Life (5 papers), Machine Learning in Healthcare (4 papers) and Breast Cancer Treatment Studies (4 papers). Tina Seto collaborates with scholars based in United States and India. Tina Seto's co-authors include Tina Hernandez‐Boussard, James D. Brooks, Douglas W. Blayney, Allison W. Kurian, Martin Seneviratne, Scarlett Lin Gomez, Amar K. Das, Manisha Desai, Michelle Ferrari and Susan Weber and has published in prestigious journals such as Journal of Clinical Oncology, Cancer and Clinical Cancer Research.

In The Last Decade

Tina Seto

23 papers receiving 428 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tina Seto United States 12 170 96 77 76 71 26 435
Travis Osterman United States 11 127 0.7× 70 0.7× 119 1.5× 64 0.8× 106 1.5× 33 504
Brandon Arnieri United States 6 175 1.0× 55 0.6× 49 0.6× 93 1.2× 71 1.0× 10 498
Jennifer Braun United States 9 145 0.9× 90 0.9× 79 1.0× 18 0.2× 167 2.4× 19 515
Aaron B. Cohen United States 14 154 0.9× 47 0.5× 70 0.9× 46 0.6× 58 0.8× 52 645
Afrooz Mazidimoradi Iran 13 257 1.5× 40 0.4× 52 0.7× 47 0.6× 20 0.3× 44 557
Sarah Jo Stephens United States 9 125 0.7× 29 0.3× 93 1.2× 45 0.6× 30 0.4× 29 387
Tobias Paul Seraphin Germany 8 102 0.6× 76 0.8× 52 0.7× 67 0.9× 19 0.3× 12 429
Roberto Vespignani Italy 6 144 0.8× 25 0.3× 33 0.4× 53 0.7× 46 0.6× 15 387
David Martin United States 12 124 0.7× 35 0.4× 131 1.7× 73 1.0× 49 0.7× 31 630
S. P. Somashekhar India 11 149 0.9× 118 1.2× 52 0.7× 156 2.1× 38 0.5× 98 670

Countries citing papers authored by Tina Seto

Since Specialization
Citations

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

Fields of papers citing papers by Tina Seto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tina Seto. 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 Tina Seto. The network helps show where Tina Seto may publish in the future.

Co-authorship network of co-authors of Tina Seto

This figure shows the co-authorship network connecting the top 25 collaborators of Tina Seto. A scholar is included among the top collaborators of Tina Seto 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 Tina Seto. Tina Seto 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.
Fleming, Scott L., et al.. (2025). Diagnostic framework to validate clinical machine learning models locally on temporally stamped data. Communications Medicine. 5(1). 261–261.
2.
Ng, Madelena Y., et al.. (2025). Development of secure infrastructure for advancing generative artificial intelligence research in healthcare at an academic medical center. Journal of the American Medical Informatics Association. 32(3). 586–588. 6 indexed citations
3.
Somani, Sulaiman, et al.. (2025). Understanding Reasons for Oral Anticoagulation Nonprescription in Atrial Fibrillation Using Large Language Models. Journal of the American Heart Association. 14(7). e040419–e040419.
4.
Sang, Shengtian, et al.. (2021). Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study. Journal of Medical Internet Research. 23(2). e23026–e23026. 10 indexed citations
5.
Blayney, Douglas W., et al.. (2021). Benchmark Method for Cost Computations Across Health Care Systems: Cost of Care per Patient per Day in Breast Cancer Care. JCO Oncology Practice. 17(10). e1403–e1412. 5 indexed citations
6.
Jung, Kenneth, Sehj Kashyap, Anand Avati, et al.. (2020). A framework for making predictive models useful in practice. Journal of the American Medical Informatics Association. 28(6). 1149–1158. 44 indexed citations
7.
Lin, Chieh‐Yu, Sujay Vennam, Natasha Purington, et al.. (2019). Genomic landscape of ductal carcinoma in situ and association with progression. Breast Cancer Research and Treatment. 178(2). 307–316. 12 indexed citations
8.
Chin, Kuo‐Kai, Ian Carroll, Karishma Desai, et al.. (2019). Integrating Adjuvant Analgesics into Perioperative Pain Practice: Results from an Academic Medical Center. Pain Medicine. 21(1). 161–170. 6 indexed citations
9.
Magnani, Christopher J., Kevin Li, Tina Seto, et al.. (2019). PSA Testing Use and Prostate Cancer Diagnostic Stage After the 2012 U.S. Preventive Services Task Force Guideline Changes. Journal of the National Comprehensive Cancer Network. 17(7). 795–803. 24 indexed citations
10.
Afghahi, Anosheh, Natasha Purington, Summer S. Han, et al.. (2018). Higher Absolute Lymphocyte Counts Predict Lower Mortality from Early-Stage Triple-Negative Breast Cancer. Clinical Cancer Research. 24(12). 2851–2858. 67 indexed citations
11.
Desai, Karishma, Ian Carroll, Steven M. Asch, et al.. (2018). Utilization and effectiveness of multimodal discharge analgesia for postoperative pain management. Journal of Surgical Research. 228. 160–169. 24 indexed citations
12.
Seneviratne, Martin, Tina Seto, Douglas W. Blayney, James D. Brooks, & Tina Hernandez‐Boussard. (2018). Architecture and Implementation of a Clinical Research Data Warehouse for Prostate Cancer. eGEMs (Generating Evidence & Methods to improve patient outcomes). 6(1). 13–13. 31 indexed citations
13.
Li, Kevin, Christopher J. Magnani, Selen Bozkurt, et al.. (2018). Practice-based evidence for factors associated with urinary incontinence following prostate cancer care.. Journal of Clinical Oncology. 36(6_suppl). 106–106. 1 indexed citations
14.
Magnani, Christopher J., Kevin Li, Tina Seto, et al.. (2018). MP40-03 CHANGES IN PROSTATE SPECIFIC ANTIGEN SCREENING AND PROSTATE CANCER DIAGNOSIS AFTER GUIDELINE CHANGES. The Journal of Urology. 199(4S).
15.
Low, Yen, Aaron C. Daugherty, Elizabeth A. Schroeder, et al.. (2016). Synergistic drug combinations from electronic health records and gene expression. Journal of the American Medical Informatics Association. 24(3). 565–576. 8 indexed citations
16.
Afghahi, Anosheh, Erna Forgó, Aya Mitani, et al.. (2015). Chromosomal copy number alterations for associations of ductal carcinoma in situ with invasive breast cancer. Breast Cancer Research. 17(1). 108–108. 19 indexed citations
17.
Afghahi, Anosheh, Maya B. Mathur, Tina Seto, et al.. (2015). Lymphopenia after adjuvant radiotherapy (RT) to predict poor survival in triple-negative breast cancer (TNBC).. Journal of Clinical Oncology. 33(15_suppl). 1069–1069. 10 indexed citations
18.
Afghahi, Anosheh, Aya Mitani, Manisha Desai, et al.. (2014). Use of the 21-gene recurrence score assay (RS) and chemotherapy (CT) across health care (HC) systems.. Journal of Clinical Oncology. 32(15_suppl). 6580–6580. 1 indexed citations
19.
Kurian, Allison W., Aya Mitani, Manisha Desai, et al.. (2013). Breast cancer treatment across health care systems: Linking electronic medical records and state registry data to enable outcomes research. Cancer. 120(1). 103–111. 47 indexed citations
20.
Weber, Susan, et al.. (2012). Oncoshare: lessons learned from building an integrated multi-institutional database for comparative effectiveness research.. PubMed. 2012. 970–8. 27 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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