Richard A. Moffitt

12.7k total citations · 2 hit papers
66 papers, 2.4k citations indexed

About

Richard A. Moffitt is a scholar working on Oncology, Cancer Research and Infectious Diseases. According to data from OpenAlex, Richard A. Moffitt has authored 66 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Oncology, 16 papers in Cancer Research and 14 papers in Infectious Diseases. Recurrent topics in Richard A. Moffitt's work include Pancreatic and Hepatic Oncology Research (20 papers), COVID-19 Clinical Research Studies (13 papers) and Cancer Genomics and Diagnostics (13 papers). Richard A. Moffitt is often cited by papers focused on Pancreatic and Hepatic Oncology Research (20 papers), COVID-19 Clinical Research Studies (13 papers) and Cancer Genomics and Diagnostics (13 papers). Richard A. Moffitt collaborates with scholars based in United States, Canada and Colombia. Richard A. Moffitt's co-authors include Jen Jen Yeh, Raoud Marayati, Keith E. Volmar, Naim U. Rashid, Silvia G. Herrera Loeza, Han Jo Kim, Katherine A. Hoadley, Christine A. Iacobuzio–Donahue, Marina Talamonti and Michael A. Hollingsworth and has published in prestigious journals such as Nature Communications, Nature Genetics and Journal of Clinical Oncology.

In The Last Decade

Richard A. Moffitt

61 papers receiving 2.4k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma

2015 1.2k citations Richard A. Moffitt, Keith E. Volmar et al. profile →
Coding long COVID: characterizing a new disease through an ICD-10 lens

2023 84 citations Emily Pfaff, Charisse Madlock‐Brown et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Richard A. Moffitt United States	23	1.5k	934	857	314	298	66	2.4k
Viktor H. Koelzer Switzerland	35	2.4k 1.6×	444 0.5×	727 0.8×	404 1.3×	611 2.1×	90	3.7k
Lorenzo Trippa United States	28	875 0.6×	680 0.7×	829 1.0×	97 0.3×	351 1.2×	120	2.8k
Moritz Jesinghaus Germany	25	1.1k 0.8×	351 0.4×	395 0.5×	492 1.6×	409 1.4×	92	2.0k
Philippe Broët France	32	787 0.5×	772 0.8×	1.0k 1.2×	138 0.4×	486 1.6×	91	3.7k
Thaer Khoury United States	32	1.5k 1.0×	1.1k 1.2×	1.1k 1.2×	420 1.3×	453 1.5×	153	3.1k
Wolfgang Schmitt Germany	26	1.4k 1.0×	753 0.8×	939 1.1×	806 2.6×	939 3.2×	96	3.4k
Benjamin Goeppert Germany	31	1.3k 0.9×	590 0.6×	880 1.0×	1.1k 3.6×	956 3.2×	105	3.0k
Marie‐Christine Mathieu France	25	978 0.7×	705 0.8×	461 0.5×	184 0.6×	403 1.4×	63	2.1k
Stefan Steurer Germany	22	392 0.3×	411 0.4×	697 0.8×	176 0.6×	464 1.6×	75	1.7k
Xiaosong Chen China	30	1.5k 1.0×	1.3k 1.4×	924 1.1×	328 1.0×	381 1.3×	163	3.2k

Countries citing papers authored by Richard A. Moffitt

Since Specialization

Citations

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

Fields of papers citing papers by Richard A. Moffitt

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard A. Moffitt. 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 Richard A. Moffitt. The network helps show where Richard A. Moffitt may publish in the future.

Co-authorship network of co-authors of Richard A. Moffitt

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

All Works

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20 of 20 papers shown

Bhatia, Abhishek, Alexander Preiss, M. Daniel Brannock, et al.. (2025). Effect of nirmatrelvir/ritonavir (Paxlovid) on hospitalization among adults with COVID-19: An electronic health record-based target trial emulation from N3C. PLoS Medicine. 22(1). e1004493–e1004493. 2 indexed citations

Yoo, Yun Jae, Carolyn T. Bramante, Jane E.B. Reusch, et al.. (2025). Association of glycemic control with Long COVID in patients with type 2 diabetes: findings from the National COVID Cohort Collaborative (N3C). BMJ Open Diabetes Research & Care. 13(1). e004536–e004536.

Powers, John, Abhishek Bhatia, Charisse Madlock‐Brown, et al.. (2025). Identifying commonalities and differences between EHR representations of PASC and ME/CFS in the RECOVER EHR cohort. Communications Medicine. 5(1). 109–109. 1 indexed citations

Preiss, Alexander, M. Daniel Brannock, John M. Baratta, et al.. (2025). Re-engineering a machine learning phenotype to adapt to the changing COVID-19 landscape: a machine learning modelling study from the N3C and RECOVER consortia. The Lancet Digital Health. 7(8). 100887–100887. 1 indexed citations

Kahn, Michael G., Tellen D. Bennett, Rachel Deer, et al.. (2025). Identifying who has long COVID in the USA: a machine learning approach using N3C data. UNC Libraries.

Stubben, Chris, et al.. (2025). Differential Control of Growth and Identity by HNF4α Isoforms in Pancreatic Ductal Adenocarcinoma. Molecular Cancer Research. 23(11). 936–952. 1 indexed citations

Preiss, Alexander, Abhishek Bhatia, John M. Baratta, et al.. (2025). Effect of Paxlovid treatment during acute COVID-19 on Long COVID onset: An EHR-based target trial emulation from the N3C and RECOVER consortia. PLoS Medicine. 22(9). e1004711–e1004711. 1 indexed citations

Martin, Blake, Peter E. DeWitt, Seth Russell, et al.. (2024). The Recent Increase in Invasive Bacterial Infections: A Report From the National COVID Cohort Collaborative. The Pediatric Infectious Disease Journal. 44(3). 217–227.

O’Neil, Shawn T., Charisse Madlock‐Brown, Kenneth J. Wilkins, et al.. (2024). Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs. npj Digital Medicine. 7(1). 296–296. 2 indexed citations

10.

Gersing, Kenneth, Sam Michael, Fred Prior, et al.. (2024). Implementing Data Governance with Multi-Modal Privacy-Preserving Record Linkages between Restricted and Public Open Enclaves. International Journal for Population Data Science. 9(5).

11.

Klein, Klara R., Anna R. Kahkoska, G. Caleb Alexander, et al.. (2024). Association of Premorbid GLP-1RA and SGLT-2i Prescription Alone and in Combination with COVID-19 Severity. Diabetes Therapy. 15(5). 1169–1186. 1 indexed citations

12.

Hill, Elaine, Hemalkumar B. Mehta, Suchetha Sharma, et al.. (2023). Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study. BMC Public Health. 23(1). 2103–2103. 25 indexed citations

13.

Mitra, Amit, Ujjal Kumar Mukherjee, Vithal Madhira, et al.. (2023). Sample average treatment effect on the treated (SATT) analysis using counterfactual explanation identifies BMT and SARS-CoV-2 vaccination as protective risk factors associated with COVID-19 severity and survival in patients with multiple myeloma. Blood Cancer Journal. 13(1). 180–180. 2 indexed citations

14.

Torre-Healy, Luke A., et al.. (2022). Subtype-Specific Spatial Descriptors of Tumor-Immune Microenvironment are Prognostic of Survival in Lung Adenocarcinoma. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). 1–5. 1 indexed citations

15.

Sun, Jun‐Ren, Rohit Patel, Vithal Madhira, et al.. (2021). COVID-19 hospitalization among people with HIV or solid organ transplant in the us. 29(1). 24–25. 4 indexed citations

16.

Mehta, Hemalkumar B., Kathleen M. Andersen, Omar Mansour, et al.. (2021). Use of Hydroxychloroquine, Remdesivir, and Dexamethasone Among Adults Hospitalized With COVID-19 in the United States. Annals of Internal Medicine. 174(10). 1395–1403. 24 indexed citations

17.

Rao, Manisha, Ki Oh, Richard A. Moffitt, et al.. (2020). Comparative single-cell RNA sequencing (scRNA-seq) reveals liver metastasis–specific targets in a patient with small intestinal neuroendocrine cancer. Molecular Case Studies. 6(2). a004978–a004978. 13 indexed citations

18.

Gupta, Rajarsi, Le Hou, Shahira Abousamra, et al.. (2020). Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor-Infiltrating Lymphocytes in Invasive Breast Cancer. American Journal Of Pathology. 190(7). 1491–1504. 73 indexed citations

19.

Rashidian, Sina, Janos Hajagos, Richard A. Moffitt, et al.. (2020). Detecting Miscoded Diabetes Diagnosis Codes in Electronic Health Records for Quality Improvement: Temporal Deep Learning Approach. JMIR Medical Informatics. 8(12). e22649–e22649. 4 indexed citations

20.

Rashid, Naim U., Xianlu L. Peng, Chong Jin, et al.. (2019). Purity Independent Subtyping of Tumors (PurIST), A Clinically Robust, Single-sample Classifier for Tumor Subtyping in Pancreatic Cancer. Clinical Cancer Research. 26(1). 82–92. 104 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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