Rachel Cavill

2.8k total citations
45 papers, 2.0k citations indexed

About

Rachel Cavill is a scholar working on Molecular Biology, Artificial Intelligence and Hematology. According to data from OpenAlex, Rachel Cavill has authored 45 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 6 papers in Artificial Intelligence and 6 papers in Hematology. Recurrent topics in Rachel Cavill's work include Metabolomics and Mass Spectrometry Studies (11 papers), Bioinformatics and Genomic Networks (7 papers) and Gene expression and cancer classification (6 papers). Rachel Cavill is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (11 papers), Bioinformatics and Genomic Networks (7 papers) and Gene expression and cancer classification (6 papers). Rachel Cavill collaborates with scholars based in Netherlands, United Kingdom and Germany. Rachel Cavill's co-authors include Hector C. Keun, Timothy M. D. Ebbels, Atanas Kamburov, Ralf Herwig, Jos Kleinjans, Jacob J. Briedé, Danyel Jennen, Jeremy K. Nicholson, Mainak Mal and Poh Koon Koh and has published in prestigious journals such as Angewandte Chemie International Edition, Nature Communications and Bioinformatics.

In The Last Decade

Rachel Cavill

42 papers receiving 2.0k citations

Peers

Rachel Cavill

HEMAT

PHYSI

Haiyu Zhang China

Óttar Rolfsson Iceland

Rui Yang China

Maren Mieth Germany

Bruno Baudin France

Piero Del Boccio Italy

Jinhua Wang China

Rudolf Oehler Austria

Mark E. McComb United States

Haiyu Zhang China

Rachel Cavill

1.5k ×1.3

215 ×0.8

HEMAT

506 ×1.9

156 ×1.0

199 ×1.4

PHYSI

Citations per year, relative to Rachel Cavill Rachel Cavill (= 1×) peers Haiyu Zhang

Countries citing papers authored by Rachel Cavill

Since Specialization

Citations

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

Fields of papers citing papers by Rachel Cavill

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rachel Cavill

This figure shows the co-authorship network connecting the top 25 collaborators of Rachel Cavill. A scholar is included among the top collaborators of Rachel Cavill 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 Rachel Cavill. Rachel Cavill 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:

20 of 20 papers shown

Kaznatcheev, Artem, et al.. (2025). Validation of polymorphic Gompertzian model of cancer through in vitro and in vivo data. PLoS ONE. 20(1). e0310844–e0310844. 3 indexed citations

Koeze, Jacqueline, Frederik Keus, Sander M. J. van Kuijk, et al.. (2024). Deep embedded clustering generalisability and adaptation for integrating mixed datatypes: two critical care cohorts. Scientific Reports. 14(1). 1045–1045. 4 indexed citations

Cavill, Rachel, Evgueni Smirnov, Michael Lenz, et al.. (2023). Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data. PLoS ONE. 18(11). e0292030–e0292030.

Ammar, Ammar, Rachel Cavill, Chris T. Evelo, & Egon Willighagen. (2023). PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity. Journal of Cheminformatics. 15(1). 31–31. 6 indexed citations

Donners, Marjo M. P. C., Joël Karel, Anton Jan van Zonneveld, et al.. (2023). Sex-specific differences in cytokine signaling pathways in circulating monocytes of cardiovascular disease patients. Atherosclerosis. 384. 117123–117123. 8 indexed citations

Dunster, Joanne L., Rachel Cavill, Michael G. Tomlinson, et al.. (2023). An agent-based approach for modelling and simulation of glycoprotein VI receptor diffusion, localisation and dimerisation in platelet lipid rafts. Scientific Reports. 13(1). 3906–3906. 4 indexed citations

Fernández, Delia I., Jingnan Huang, Robert Ahrends, et al.. (2023). High-throughput assessment identifying major platelet Ca2+ entry pathways via tyrosine kinase-linked and G protein-coupled receptors. Cell Calcium. 112. 102738–102738. 11 indexed citations

Cavill, Rachel, Annelien Duits, Sebastian Köhler, et al.. (2022). Machine learning-based prediction of cognitive outcomes in de novo Parkinson’s disease. npj Parkinson s Disease. 8(1). 150–150. 25 indexed citations

Ammar, Ammar, Rachel Cavill, Chris T. Evelo, & Egon Willighagen. (2022). PSnpBind: a database of mutated binding site protein–ligand complexes constructed using a multithreaded virtual screening workflow. Journal of Cheminformatics. 14(1). 8–8. 6 indexed citations

10.

Huang, Jingnan, Frauke Swieringa, Fiorella A. Solari, et al.. (2021). Assessment of a complete and classified platelet proteome from genome-wide transcripts of human platelets and megakaryocytes covering platelet functions. Scientific Reports. 11(1). 12358–12358. 63 indexed citations

11.

Cavill, Rachel, et al.. (2021). Exploring the influence of cytosolic and membrane FAK activation on YAP/TAZ nuclear translocation. Biophysical Journal. 120(20). 4360–4377. 8 indexed citations

12.

Driessens, Kurt, Evgueni Smirnov, Michael Lenz, et al.. (2020). Use of deep learning methods to translate drug-induced gene expression changes from rat to human primary hepatocytes. PLoS ONE. 15(8). e0236392–e0236392. 4 indexed citations

13.

Brouns, Sanne L. N., Johanna P. van Geffen, Elena Campello, et al.. (2020). Platelet-primed interactions of coagulation and anticoagulation pathways in flow-dependent thrombus formation. Scientific Reports. 10(1). 11910–11910. 29 indexed citations

14.

Monnery, Bryn D., Michael Wright, Rachel Cavill, et al.. (2017). Cytotoxicity of polycations: Relationship of molecular weight and the hydrolytic theory of the mechanism of toxicity. International Journal of Pharmaceutics. 521(1-2). 249–258. 187 indexed citations

15.

Deferme, Lize, Jacob J. Briedé, Sandra M.H. Claessen, Rachel Cavill, & Jos Kleinjans. (2015). Cell line-specific oxidative stress in cellular toxicity: A toxicogenomics-based comparison between liver and colon cell models. Toxicology in Vitro. 29(5). 845–855. 13 indexed citations

16.

Aschauer, Lydia, Leonhard Gruber, Walter Pfaller, et al.. (2013). Delineation of the Key Aspects in the Regulation of Epithelial Monolayer Formation. Molecular and Cellular Biology. 33(13). 2535–2550. 59 indexed citations

17.

Cavill, Rachel, Jos Kleinjans, & Jacob J. Briedé. (2013). DTW4Omics: Comparing Patterns in Biological Time Series. PLoS ONE. 8(8). e71823–e71823. 17 indexed citations

18.

Deferme, Lize, Jacob J. Briedé, Sandra M.H. Claessen, et al.. (2013). Time series analysis of oxidative stress response patterns in HepG2: A toxicogenomics approach. Toxicology. 306. 24–34. 30 indexed citations

19.

Cavill, Rachel, Atanas Kamburov, James K. Ellis, et al.. (2011). Consensus-Phenotype Integration of Transcriptomic and Metabolomic Data Implies a Role for Metabolism in the Chemosensitivity of Tumour Cells. PLoS Computational Biology. 7(3). e1001113–e1001113. 72 indexed citations

20.

Ellis, James K., Toby J. Athersuch, Rachel Cavill, et al.. (2010). Metabolic response to low-level toxicant exposure in a novel renal tubuleepithelial cell system. Molecular BioSystems. 7(1). 247–257. 44 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.

Explore authors with similar magnitude of impact