Ryan Copping

641 total citations · 1 hit paper
9 papers, 355 citations indexed

About

Ryan Copping is a scholar working on Statistics and Probability, Cancer Research and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Ryan Copping has authored 9 papers receiving a total of 355 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Statistics and Probability, 4 papers in Cancer Research and 3 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Ryan Copping's work include Cancer Genomics and Diagnostics (4 papers), Statistical Methods in Clinical Trials (3 papers) and Health Systems, Economic Evaluations, Quality of Life (2 papers). Ryan Copping is often cited by papers focused on Cancer Genomics and Diagnostics (4 papers), Statistical Methods in Clinical Trials (3 papers) and Health Systems, Economic Evaluations, Quality of Life (2 papers). Ryan Copping collaborates with scholars based in United States, Switzerland and United Kingdom. Ryan Copping's co-authors include Brandon Arnieri, Michael W. Lu, Samuel Whipple, William B. Capra, Shemra Rizzo, Ruishan Liu, Navdeep Pal, James Zou, Arturo López Pineda and Ying Lü and has published in prestigious journals such as Nature, Nature Medicine and Nature Communications.

In The Last Decade

Ryan Copping

8 papers receiving 345 citations

Hit Papers

align trajectories

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.

Evaluating eligibility criteria of oncology trials using real-world data and AI

2021 184 citations Ruishan Liu, Shemra Rizzo et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ryan Copping United States	7	104	95	63	62	55	9	355
Samuel Whipple United States	5	100 1.0×	90 0.9×	53 0.8×	43 0.7×	56 1.0×	9	312
Brandon Arnieri United States	6	117 1.1×	112 1.2×	93 1.5×	55 0.9×	71 1.3×	10	498
Arturo López Pineda United States	8	45 0.4×	52 0.5×	37 0.6×	129 2.1×	59 1.1×	16	366
Stuart Bailey United States	11	175 1.7×	42 0.4×	56 0.9×	59 1.0×	49 0.9×	15	524
Lauren B. Becnel United States	10	27 0.3×	37 0.4×	40 0.6×	58 0.9×	76 1.4×	18	437
Travis Osterman United States	11	18 0.2×	54 0.6×	64 1.0×	70 1.1×	106 1.9×	33	504
G. Caleb Alexander United States	8	57 0.5×	169 1.8×	18 0.3×	13 0.2×	54 1.0×	11	391
Vivek A. Rudrapatna United States	11	22 0.2×	32 0.3×	21 0.3×	43 0.7×	44 0.8×	33	449
Alind Gupta Canada	10	31 0.3×	47 0.5×	10 0.2×	40 0.6×	28 0.5×	20	300
Melissa D. Curtis United States	6	49 0.5×	68 0.7×	78 1.2×	30 0.5×	30 0.5×	8	427

Countries citing papers authored by Ryan Copping

Since Specialization

Citations

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

Fields of papers citing papers by Ryan Copping

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ryan Copping

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

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Liu, Ruishan, Shemra Rizzo, Lisa Wang, et al.. (2024). Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers. Nature Communications. 15(1). 10884–10884.

2.

Liu, Ruishan, Lisa Wang, Shemra Rizzo, et al.. (2024). Systematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients. Cell Reports Medicine. 5(3). 101444–101444. 8 indexed citations

3.

Mitra, Robin, Sarah F. McGough, Tapabrata Chakraborti, et al.. (2023). Learning from data with structured missingness. Nature Machine Intelligence. 5(1). 13–23. 30 indexed citations

4.

Liu, Ruishan, Shemra Rizzo, Sarah Waliany, et al.. (2022). Systematic pan-cancer analysis of mutation–treatment interactions using large real-world clinicogenomics data. Nature Medicine. 28(8). 1656–1661. 21 indexed citations

5.

Wu, Kevin, Eric Wu, Marek Dąbrowski, et al.. (2022). Machine Learning Prediction of Clinical Trial Operational Efficiency. The AAPS Journal. 24(3). 57–57. 14 indexed citations

6.

Liu, Ruishan, Shemra Rizzo, Samuel Whipple, et al.. (2021). Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature. 592(7855). 629–633. 184 indexed citations breakdown →

7.

McGough, Sarah F., Devin Incerti, Svetlana Lyalina, et al.. (2021). Penalized regression for left‐truncated and right‐censored survival data. Statistics in Medicine. 40(25). 5487–5500. 25 indexed citations

8.

Gaire, Fabien, et al.. (2020). 1381P Spatial-statistics-based modeling for predicting treatment response in non-small cell lung cancer (NSCLC) patients using H&E pathology images. Annals of Oncology. 31. S879–S879. 1 indexed citations

9.

Carrigan, Gillis, Samuel Whipple, William B. Capra, et al.. (2019). Using Electronic Health Records to Derive Control Arms for Early Phase Single‐Arm Lung Cancer Trials: Proof‐of‐Concept in Randomized Controlled Trials. Clinical Pharmacology & Therapeutics. 107(2). 369–377. 72 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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