Jake T. M. Pearce

2.2k total citations · 1 hit paper
15 papers, 1.3k citations indexed

About

Jake T. M. Pearce is a scholar working on Molecular Biology, Spectroscopy and Biomedical Engineering. According to data from OpenAlex, Jake T. M. Pearce has authored 15 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 4 papers in Spectroscopy and 3 papers in Biomedical Engineering. Recurrent topics in Jake T. M. Pearce's work include Metabolomics and Mass Spectrometry Studies (13 papers), Analytical Chemistry and Chromatography (3 papers) and Advanced Chemical Sensor Technologies (3 papers). Jake T. M. Pearce is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (13 papers), Analytical Chemistry and Chromatography (3 papers) and Advanced Chemical Sensor Technologies (3 papers). Jake T. M. Pearce collaborates with scholars based in United Kingdom, United States and Germany. Jake T. M. Pearce's co-authors include Jeremy K. Nicholson, Elaine Holmes, John C. Lindon, Matthew R. Lewis, Timothy M. D. Ebbels, Beatriz Jiménez, Hector C. Keun, Manfred Spraul, Hartmut Schäfer and Anthony C. Dona and has published in prestigious journals such as Bioinformatics, Journal of Molecular Biology and Analytical Chemistry.

In The Last Decade

Jake T. M. Pearce

15 papers receiving 1.3k citations

Hit Papers

Precision High-Throughput Proton NMR Spectroscopy of Huma... 2014 2026 2018 2022 2014 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jake T. M. Pearce United Kingdom 14 973 242 231 185 153 15 1.3k
Joanna Godzień Spain 21 1.0k 1.1× 364 1.5× 192 0.8× 155 0.8× 205 1.3× 50 1.5k
Michał Ciborowski Poland 26 1.1k 1.1× 223 0.9× 297 1.3× 163 0.9× 121 0.8× 115 1.9k
Shucha Zhang United States 19 1.4k 1.5× 393 1.6× 397 1.7× 270 1.5× 223 1.5× 25 2.0k
Raymond Ramaker Netherlands 9 793 0.8× 271 1.1× 100 0.4× 106 0.6× 108 0.7× 9 1.2k
S. C. Connor United Kingdom 12 986 1.0× 265 1.1× 244 1.1× 150 0.8× 100 0.7× 13 1.3k
Joana Pinto Portugal 23 922 0.9× 238 1.0× 196 0.8× 147 0.8× 262 1.7× 94 1.9k
Magda Bictash United Kingdom 17 1.5k 1.5× 137 0.6× 563 2.4× 138 0.7× 210 1.4× 25 2.2k
Alla Karnovsky United States 26 1.7k 1.7× 146 0.6× 280 1.2× 357 1.9× 91 0.6× 59 2.5k
Theo Reijmers Netherlands 24 1.2k 1.2× 510 2.1× 144 0.6× 122 0.7× 163 1.1× 43 1.6k
Donald G. Robertson United States 24 1.4k 1.4× 300 1.2× 230 1.0× 437 2.4× 182 1.2× 49 2.0k

Countries citing papers authored by Jake T. M. Pearce

Since Specialization
Citations

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

Fields of papers citing papers by Jake T. M. Pearce

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jake T. M. Pearce. 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 Jake T. M. Pearce. The network helps show where Jake T. M. Pearce may publish in the future.

Co-authorship network of co-authors of Jake T. M. Pearce

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

All Works

15 of 15 papers shown
1.
Blaise, Benjamin J., Gonçalo dos Santos Correia, Izabella Surowiec, et al.. (2021). Statistical analysis in metabolic phenotyping. Nature Protocols. 16(9). 4299–4326. 60 indexed citations
2.
Correia, Gonçalo dos Santos, Caroline Sands, Stéphane Camuzeaux, et al.. (2021). peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC–MS profiling datasets. Bioinformatics. 37(24). 4886–4888. 21 indexed citations
3.
Sands, Caroline, Gonçalo dos Santos Correia, Noureddin Sadawi, et al.. (2019). The nPYc-Toolbox, a Python module for the pre-processing, quality-control and analysis of metabolic profiling datasets. Bioinformatics. 35(24). 5359–5360. 20 indexed citations
4.
Hoffmann, Nils, Timo Sachsenberg, Jürgen Hartler, et al.. (2019). mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics. Analytical Chemistry. 91(5). 3302–3310. 41 indexed citations
5.
Jiménez, Beatriz, Elaine Holmes, Samantha Lodge, et al.. (2018). Quantitative Lipoprotein Subclass and Low Molecular Weight Metabolite Analysis in Human Serum and Plasma by 1H NMR Spectroscopy in a Multilaboratory Trial. Analytical Chemistry. 90(20). 11962–11971. 162 indexed citations
6.
Turner, Paul, Isabel Skypala, Stephen R. Durham, et al.. (2018). CHANGES IN METABONOMIC PROFILE DURING PEANUT-INDUCED ANAPHYLAXIS AND CORRELATION WITH SYMPTOM. Journal of Allergy and Clinical Immunology. 141(2). AB85–AB85. 1 indexed citations
7.
Lewis, Matthew R., Jake T. M. Pearce, Konstantina Spagou, et al.. (2016). Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping. Analytical Chemistry. 88(18). 9004–9013. 93 indexed citations
8.
Blaise, Benjamin J., Gonçalo dos Santos Correia, Adrienne Tin, et al.. (2016). Power Analysis and Sample Size Determination in Metabolic Phenotyping. Analytical Chemistry. 88(10). 5179–5188. 81 indexed citations
9.
Dona, Anthony C., Beatriz Jiménez, Hartmut Schäfer, et al.. (2014). Precision High-Throughput Proton NMR Spectroscopy of Human Urine, Serum, and Plasma for Large-Scale Metabolic Phenotyping. Analytical Chemistry. 86(19). 9887–9894. 357 indexed citations breakdown →
10.
Merrifield, Claire A, Marie C. Lewis, Sandrine P. Claus, et al.. (2012). Weaning diet induces sustained metabolic phenotype shift in the pig and influences host response to Bifidobacterium lactis NCC2818. Gut. 62(6). 842–851. 17 indexed citations
11.
Want, Elizabeth J., Muireann Coen, Perrine Masson, et al.. (2010). Ultra Performance Liquid Chromatography-Mass Spectrometry Profiling of Bile Acid Metabolites in Biofluids: Application to Experimental Toxicology Studies. Analytical Chemistry. 82(12). 5282–5289. 70 indexed citations
12.
Pearce, Jake T. M., Toby J. Athersuch, Timothy M. D. Ebbels, et al.. (2008). Robust Algorithms for Automated Chemical Shift Calibration of 1D 1H NMR Spectra of Blood Serum. Analytical Chemistry. 80(18). 7158–7162. 48 indexed citations
13.
Coen, Muireann, T. Andrew Clayton, Cynthia M. Rohde, et al.. (2007). The Mechanism of Galactosamine Toxicity Revisited; A Metabonomic Study. Journal of Proteome Research. 6(7). 2711–2719. 52 indexed citations
14.
Lindon, John C., Hector C. Keun, Timothy M. D. Ebbels, et al.. (2005). The Consortium for Metabonomic Toxicology (COMET): Aims, Activities and Achievements. Pharmacogenomics. 6(7). 691–699. 200 indexed citations
15.
Read, Jon, Jake T. M. Pearce, Xiaochun Li, et al.. (2001). The crystal structure of human phosphoglucose isomerase at 1.6 Å resolution: implications for catalytic mechanism, cytokine activity and haemolytic anaemia. Journal of Molecular Biology. 309(2). 447–463. 89 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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