James Sun

8.0k total citations
16 papers, 715 citations indexed

About

James Sun is a scholar working on Cancer Research, Oncology and Pathology and Forensic Medicine. According to data from OpenAlex, James Sun has authored 16 papers receiving a total of 715 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Cancer Research, 6 papers in Oncology and 5 papers in Pathology and Forensic Medicine. Recurrent topics in James Sun's work include Cancer Genomics and Diagnostics (14 papers), Genetic factors in colorectal cancer (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). James Sun is often cited by papers focused on Cancer Genomics and Diagnostics (14 papers), Genetic factors in colorectal cancer (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). James Sun collaborates with scholars based in United States, France and United Kingdom. James Sun's co-authors include Jeffrey S. Ross, Vincent A. Miller, Garrett M. Frampton, David Fabrizio, Phil Stephens, Eric M. Sanford, Kyle Gowen, Siraj M. Ali, Alexa B. Schrock and Philip J. Stephens and has published in prestigious journals such as Journal of Clinical Oncology, Blood and Cancer.

In The Last Decade

James Sun

16 papers receiving 707 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James Sun United States 8 496 295 293 196 147 16 715
Beth Z. Clark United States 17 332 0.7× 307 1.0× 174 0.6× 292 1.5× 88 0.6× 47 782
Erna Forgó United States 11 332 0.7× 184 0.6× 256 0.9× 249 1.3× 209 1.4× 21 672
Kyle Gowen United States 14 712 1.4× 354 1.2× 465 1.6× 294 1.5× 255 1.7× 24 1.1k
Eiichi Sasaki Japan 17 438 0.9× 188 0.6× 372 1.3× 116 0.6× 242 1.6× 71 898
Loralee McMahon United States 13 280 0.6× 157 0.5× 225 0.8× 74 0.4× 223 1.5× 26 608
Alexandr O. Ivantsov Russia 16 360 0.7× 248 0.8× 263 0.9× 172 0.9× 257 1.7× 51 715
Sohail Balasubramanian United States 14 471 0.9× 371 1.3× 433 1.5× 186 0.9× 368 2.5× 24 1.1k
Hayley Robinson United States 6 233 0.5× 230 0.8× 307 1.0× 121 0.6× 308 2.1× 8 718
Kenneth J. Craddock Canada 12 261 0.5× 163 0.6× 250 0.9× 121 0.6× 162 1.1× 28 618
Carla Pinto Portugal 19 320 0.6× 269 0.9× 183 0.6× 250 1.3× 299 2.0× 47 733

Countries citing papers authored by James Sun

Since Specialization
Citations

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

Fields of papers citing papers by James Sun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James Sun

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

All Works

16 of 16 papers shown
1.
Trabucco, Sally E., Kyle Gowen, Sophia L. Maund, et al.. (2019). A Novel Next-Generation Sequencing Approach to Detecting Microsatellite Instability and Pan-Tumor Characterization of 1000 Microsatellite Instability–High Cases in 67,000 Patient Samples. Journal of Molecular Diagnostics. 21(6). 1053–1066. 162 indexed citations
2.
Sun, James, Yuting He, Eric M. Sanford, et al.. (2018). A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal. PLoS Computational Biology. 14(2). e1005965–e1005965. 177 indexed citations
3.
Ross, Jeffrey S., Marwan Fakih, Siraj M. Ali, et al.. (2018). Targeting HER2 in colorectal cancer: The landscape of amplification and short variant mutations in ERBB2 and ERBB3. Cancer. 124(7). 1358–1373. 149 indexed citations
4.
Yip, Wai‐Ki, Joel Skoletsky, Pei Ma, et al.. (2018). Abstract 1607: An ERBB2 follow-on companion diagnostic for clinical care of patients with breast cancer. Cancer Research. 78(13_Supplement). 1607–1607. 1 indexed citations
5.
Tarlock, Katherine, Yuting He, James Sun, et al.. (2017). Recurrent Copy Number Variants Are Highly Prevalent in Acute Myeloid Leukemia. Blood. 130. 3800–3800. 1 indexed citations
6.
Elvin, Julia A., Yuting He, James Sun, et al.. (2017). Comprehensive genomic profiling (CGP) with loss of heterozygosity (LOH) to identify therapeutically relevant subsets of ovarian cancer (OC).. Journal of Clinical Oncology. 35(15_suppl). 5512–5512. 10 indexed citations
7.
Mayor, Paul, Laurie M. Gay, Erica Gornstein, et al.. (2017). BRCA1/2 reversion mutations revealed in breast and gynecologic cancers sequenced during routine clinical care using tissue or liquid biopsy.. Journal of Clinical Oncology. 35(15_suppl). 5551–5551. 2 indexed citations
8.
Gay, Laurie M., David Fabrizio, Garrett M. Frampton, et al.. (2017). Mutational burden of tumors with primary site unknown.. Journal of Clinical Oncology. 35(15_suppl). 3039–3039. 3 indexed citations
9.
Frampton, Garrett M., Alexa B. Schrock, Zachary R. Chalmers, et al.. (2016). Comprehensive genomic profiling (CGP) to assess mutational load in gastric and esophageal adenocarcinomas: Implications for immunotherapies.. Journal of Clinical Oncology. 34(4_suppl). 66–66. 2 indexed citations
10.
George, Thomas J., Garrett M. Frampton, James Sun, et al.. (2016). Tumor mutational burden as a potential biomarker for PD1/PD-L1 therapy in colorectal cancer.. Journal of Clinical Oncology. 34(15_suppl). 3587–3587. 25 indexed citations
11.
Santin, Alessandro D., Kathleen N. Moore, Camille C. Gunderson, et al.. (2016). Immunotherapy (IO) versus targeted therapy triage in endometrial adenocarcinoma (EA) by concurrent assessment of tumor mutation burden (TMB), microsatellite instability (MSI) status, and targetable genomic alterations (GA).. Journal of Clinical Oncology. 34(15_suppl). 5591–5591. 2 indexed citations
12.
Spigel, David R., Alexa B. Schrock, David Fabrizio, et al.. (2016). Total mutation burden (TMB) in lung cancer (LC) and relationship with response to PD-1/PD-L1 targeted therapies.. Journal of Clinical Oncology. 34(15_suppl). 9017–9017. 147 indexed citations
13.
Hall, Michael J., Kyle Gowen, Eric M. Sanford, et al.. (2016). Evaluation of microsatellite instability (MSI) status in 11,573 diverse solid tumors using comprehensive genomic profiling (CGP).. Journal of Clinical Oncology. 34(15_suppl). 1523–1523. 14 indexed citations
14.
Hall, Michael J., Eric A. Ross, Jeff Boyd, et al.. (2015). Germline variants in cancer risk genes detected by NGS-based comprehensive tumor genomic profiling (CGP).. Journal of Clinical Oncology. 33(15_suppl). 11084–11084. 6 indexed citations
15.
Dougherty, Brian, Zhongwu Lai, Jonathan A. Ledermann, et al.. (2015). Abstract 611: Exploratory analyses suggest ovarian tumors with somatic or germline loss of function mutations in BRCA1 or BRCA2 are biologically similar and sensitive to PARP inhibition. Cancer Research. 75(15_Supplement). 611–611. 3 indexed citations
16.

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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