James W.T. Yates

102 papers receiving 3.7k citations

James W.T. Yates's Hit Papers

Preclinical Comparison of Osimertinib with Other EGFR-TKIs in EGFR-Mutant NSCLC Brain Metastases Models, and Early Evidence of Clinical Brain Metastases Activity 2016 · 610 citations
6100+3+6Years since publication200400600

Peers

James W.T. Yates
Comparison fields: 5 of 91
  • Pulmonary and Respiratory Medicine 885
  • Modeling and Simulation 90
  • Pharmacology 139
  • Oncology 282
  • Molecular Biology 444
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Citations per year

Countries citing papers authored by James W.T. Yates

Since Specialization
Citations

This map shows the geographic impact of James W.T. Yates'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 W.T. Yates with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James W.T. Yates more than expected).

Fields of papers citing papers by James W.T. Yates

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside James W.T. Yates, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with James W.T. Yates Line = papers co-authored together James W.T. Yates links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 106 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Preclinical Comparison of Osimertinib with Other EGFR-TKIs in EGFR-Mutant NSCLC Brain Metastases Models, and Early Evidence of Clinical Brain Metastases Activity
Hit paper breakdown →
2016610
2 2012419
3 2018276
4 2020178
5 2011175
6 2011148
7 2022141
8 2017133
9 2019126
10 2011120
11 2015113
12 201893
13 202272
14 201753
15 201152
16 201352
17 201151
18 201550
19 201949
20 201148

About James W.T. Yates

James W.T. Yates is a scholar working on Modeling and Simulation, Oncology, Statistics and Probability, Control and Systems Engineering and Radiology, Nuclear Medicine and Imaging, having authored 106 papers that have together received 3.9k indexed citations. Recurring topics across this work include Mathematical Biology Tumor Growth (13 papers), PI3K/AKT/mTOR signaling in cancer (7 papers), Control Systems and Identification (6 papers), Lung Cancer Treatments and Mutations (6 papers), Monoclonal and Polyclonal Antibodies Research (5 papers), Statistical Methods in Clinical Trials (4 papers), Advanced Chemical Sensor Technologies (3 papers) and PARP inhibition in cancer therapy (3 papers). The work is most often cited by research in Pulmonary and Respiratory Medicine (885 citations), Modeling and Simulation (90 citations), Pharmacology (139 citations), Oncology (282 citations) and Molecular Biology (444 citations). James W.T. Yates has collaborated with scholars based in United Kingdom, United States and Switzerland. Frequent co-authors include Peter G. Ballard, Darren A.E. Cross, Pasi Antero Janne, James Chih‐Hsin Yang, Mireille Cantarini, Peter Johnström, Matthew Grist, Dong‐Wan Kim, Jonas Malmquist and Kenneth Stephen Thress. Their work appears in journals such as CPT Pharmacometrics & Systems Pharmacology, Computer Methods and Programs in Biomedicine, Journal of Pharmaceutical Sciences, Cancer Chemotherapy and Pharmacology and European Journal of Cancer.

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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