Chris Harbron

2.2k total citations
41 papers, 908 citations indexed

About

Chris Harbron is a scholar working on Oncology, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Chris Harbron has authored 41 papers receiving a total of 908 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Oncology, 8 papers in Molecular Biology and 8 papers in Computational Theory and Mathematics. Recurrent topics in Chris Harbron's work include Statistical Methods in Clinical Trials (8 papers), Computational Drug Discovery Methods (6 papers) and Lymphoma Diagnosis and Treatment (6 papers). Chris Harbron is often cited by papers focused on Statistical Methods in Clinical Trials (8 papers), Computational Drug Discovery Methods (6 papers) and Lymphoma Diagnosis and Treatment (6 papers). Chris Harbron collaborates with scholars based in United Kingdom, Switzerland and United States. Chris Harbron's co-authors include Margaret H. Veldman-Jones, Elizabeth A. Harrington, Mark Wappett, Michael Dymond, Gayle Marshall, J. Carl Barrett, Roz Brant, Claire Rooney, Hollie Emery and Catherine Geh and has published in prestigious journals such as Nature Medicine, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Chris Harbron

38 papers receiving 887 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chris Harbron United Kingdom 18 266 195 150 128 123 41 908
Frank Pétavy Netherlands 19 234 0.9× 202 1.0× 74 0.5× 231 1.8× 66 0.5× 33 1.1k
Fiorella Guadagni Italy 18 320 1.2× 237 1.2× 149 1.0× 140 1.1× 80 0.7× 43 989
Nathan Enas United States 16 358 1.3× 492 2.5× 174 1.2× 176 1.4× 183 1.5× 25 1.4k
Zhu Feng China 3 518 1.9× 212 1.1× 261 1.7× 222 1.7× 61 0.5× 9 1.2k
Nitin Roper United States 16 461 1.7× 421 2.2× 187 1.2× 185 1.4× 76 0.6× 44 1.2k
Haocheng Li Canada 18 382 1.4× 394 2.0× 357 2.4× 262 2.0× 121 1.0× 58 1.1k
Sophie Callies United States 20 601 2.3× 321 1.6× 108 0.7× 161 1.3× 42 0.3× 44 1.2k
Stella Somiari United States 17 722 2.7× 237 1.2× 390 2.6× 93 0.7× 59 0.5× 28 1.4k
Melissa Zhao United States 8 165 0.6× 257 1.3× 372 2.5× 198 1.5× 108 0.9× 15 909
Bin Yao China 16 409 1.5× 326 1.7× 133 0.9× 297 2.3× 46 0.4× 69 1.2k

Countries citing papers authored by Chris Harbron

Since Specialization
Citations

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

Fields of papers citing papers by Chris Harbron

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chris Harbron

This figure shows the co-authorship network connecting the top 25 collaborators of Chris Harbron. A scholar is included among the top collaborators of Chris Harbron 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 Chris Harbron. Chris Harbron 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.
Chakraborti, Tapabrata, Christopher R. S. Banerji, Robin Mitra, et al.. (2025). Personalized uncertainty quantification in artificial intelligence. Nature Machine Intelligence. 7(4). 522–530. 6 indexed citations
2.
Banerji, Christopher R. S., et al.. (2025). Clinicians must participate in the development of multimodal AI. EClinicalMedicine. 84. 103252–103252.
3.
Chakraborti, Tapabrata, et al.. (2024). Deep learning as Ricci flow. Scientific Reports. 14(1). 23383–23383.
4.
Harbron, Chris, et al.. (2023). Updating the probability of study success for combination therapies using related combination study data. Statistical Methods in Medical Research. 32(4). 712–731. 1 indexed citations
5.
Banerji, Christopher R. S., Tapabrata Chakraborti, Chris Harbron, & Ben D. MacArthur. (2023). Clinical AI tools must convey predictive uncertainty for each individual patient. Nature Medicine. 29(12). 2996–2998. 21 indexed citations
6.
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
7.
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
8.
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
9.
Lea, Simon, Li J, Jonathan Plumb, et al.. (2020). P38 MAPK and glucocorticoid receptor crosstalk in bronchial epithelial cells. Journal of Molecular Medicine. 98(3). 361–374. 24 indexed citations
10.
Pott, Christiane, Laurie H. Sehn, David Belada, et al.. (2019). MRD response in relapsed/refractory FL after obinutuzumab plus bendamustine or bendamustine alone in the GADOLIN trial. Leukemia. 34(2). 522–532. 20 indexed citations
11.
Love, Sharon, Sarah Brown, Christopher J. Weir, et al.. (2017). Embracing model-based designs for dose-finding trials. British Journal of Cancer. 117(3). 332–339. 44 indexed citations
12.
Nomura, Hiroyuki, Fumio Kataoka, Daisuke Aoki, et al.. (2016). Expression of potential biomarkers associated with homologous recombination repair in patients with ovarian or triple-negative breast cancer. Cancer Biomarkers. 16(1). 145–152. 9 indexed citations
13.
Veldman-Jones, Margaret H., Roz Brant, Claire Rooney, et al.. (2015). Evaluating Robustness and Sensitivity of the NanoString Technologies nCounter Platform to Enable Multiplexed Gene Expression Analysis of Clinical Samples. Cancer Research. 75(13). 2587–2593. 234 indexed citations
14.
Veldman-Jones, Margaret H., Zhongwu Lai, Mark Wappett, et al.. (2014). Reproducible, Quantitative, and Flexible Molecular Subtyping of Clinical DLBCL Samples Using the NanoString nCounter System. Clinical Cancer Research. 21(10). 2367–2378. 54 indexed citations
15.
Dearden, Simon, Chris Harbron, Darren Hodgson, et al.. (2013). Validation of the BRCA1 antibody MS110 and the utility of BRCA1 as a patient selection biomarker in immunohistochemical analysis of breast and ovarian tumours. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 462(3). 269–279. 20 indexed citations
16.
Gordon, Mark Forrest, Thomas A. Comery, Igor D. Grachev, et al.. (2013). The Coalition Against Major Diseases: Dopamine Transporter Neuroimaging as an Enrichment Biomarker To Enable Parkinson's Disease Clinical Trials (P06.049). Neurology. 80(7_supplement). 1 indexed citations
17.
Peers, Ian, et al.. (2012). In search of preclinical robustness. Nature Reviews Drug Discovery. 11(10). 733–734. 34 indexed citations
18.
Harbron, Chris, Simon Lea, George Booth, et al.. (2011). Synergistic Effects of p38 Mitogen-Activated Protein Kinase Inhibition with a Corticosteroid in Alveolar Macrophages from Patients with Chronic Obstructive Pulmonary Disease. Journal of Pharmacology and Experimental Therapeutics. 338(3). 732–740. 67 indexed citations
19.
Zhuang, Joanna, Krina T. Zondervan, Fredrik Nyberg, et al.. (2010). Optimizing the power of genome‐wide association studies by using publicly available reference samples to expand the control group. Genetic Epidemiology. 34(4). 319–326. 12 indexed citations
20.
Harbron, Chris. (2010). A flexible unified approach to the analysis of pre‐clinical combination studies. Statistics in Medicine. 29(16). 1746–1756. 26 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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