J. Brezmes

3.5k total citations
85 papers, 2.8k citations indexed

About

J. Brezmes is a scholar working on Biomedical Engineering, Electrical and Electronic Engineering and Bioengineering. According to data from OpenAlex, J. Brezmes has authored 85 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Biomedical Engineering, 36 papers in Electrical and Electronic Engineering and 35 papers in Bioengineering. Recurrent topics in J. Brezmes's work include Advanced Chemical Sensor Technologies (62 papers), Analytical Chemistry and Sensors (35 papers) and Gas Sensing Nanomaterials and Sensors (34 papers). J. Brezmes is often cited by papers focused on Advanced Chemical Sensor Technologies (62 papers), Analytical Chemistry and Sensors (35 papers) and Gas Sensing Nanomaterials and Sensors (34 papers). J. Brezmes collaborates with scholars based in Spain, Czechia and United Kingdom. J. Brezmes's co-authors include Xavier Correig, Eduard Llobet, X Vilanova, J.E. Sueiras, P. Ivanov, María Vinaixa, J. Calderer, Jaromír Hubálek, Nicolau Cañellas and Radu Ionescu and has published in prestigious journals such as Bioinformatics, PLoS ONE and Analytical Chemistry.

In The Last Decade

J. Brezmes

82 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J. Brezmes Spain 33 1.7k 1.2k 878 516 467 85 2.8k
Benachir Bouchikhi Morocco 35 2.0k 1.2× 1.3k 1.1× 593 0.7× 593 1.1× 381 0.8× 121 3.4k
Alisa Rudnitskaya Portugal 41 2.8k 1.7× 799 0.7× 1.2k 1.3× 520 1.0× 785 1.7× 116 4.4k
Norman M. Ratcliffe United Kingdom 31 1.8k 1.1× 1.1k 0.9× 574 0.7× 996 1.9× 506 1.1× 70 3.6k
A. D’Amico Italy 39 3.3k 2.0× 2.2k 1.8× 1.3k 1.5× 383 0.7× 682 1.5× 176 4.9k
Fredrik Winquist Sweden 37 3.4k 2.0× 1.6k 1.3× 1.6k 1.8× 334 0.6× 813 1.7× 110 4.4k
Claudimir Lúcio do Lago Brazil 32 2.5k 1.5× 1.1k 0.9× 1.0k 1.2× 578 1.1× 613 1.3× 120 3.9k
Andrey Legin Russia 45 3.8k 2.3× 1.6k 1.3× 2.2k 2.5× 535 1.0× 1.0k 2.2× 223 6.3k
Tomasz Ligor Poland 29 2.8k 1.7× 1.1k 0.9× 331 0.4× 738 1.4× 1.2k 2.6× 74 3.7k
Giorgio Pennazza Italy 28 2.0k 1.2× 945 0.8× 324 0.4× 371 0.7× 495 1.1× 136 2.7k
Manel del Valle Spain 45 3.4k 2.1× 2.7k 2.2× 2.3k 2.6× 1.6k 3.1× 944 2.0× 221 6.8k

Countries citing papers authored by J. Brezmes

Since Specialization
Citations

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

Fields of papers citing papers by J. Brezmes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. Brezmes

This figure shows the co-authorship network connecting the top 25 collaborators of J. Brezmes. A scholar is included among the top collaborators of J. Brezmes 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 J. Brezmes. J. Brezmes 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.
Kloet, Frans van der, Saer Samanipour, Pierre‐Hugues Stefanuto, et al.. (2025). GcDUO: an open-source software for GC × GC–MS data analysis. Briefings in Bioinformatics. 26(2).
2.
Correig, Eudald, et al.. (2024). Easy‐Amanida: An R Shiny application for the meta‐analysis of aggregate results in clinical metabolomics using Amanida and Webchem. Research Synthesis Methods. 15(4). 687–699. 1 indexed citations
3.
Ramírez, Noelia, et al.. (2024). SPME arrow-based extraction for enhanced targeted and untargeted urinary volatilomics. Analytica Chimica Acta. 1329. 343261–343261. 1 indexed citations
4.
Gumà, Josep, et al.. (2023). A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies’ Intercomparison. Metabolites. 13(12). 1167–1167. 1 indexed citations
5.
Brezmes, J., et al.. (2022). The untargeted urine volatilome for biomedical applications: methodology and volatilome database. Biological Procedures Online. 24(1). 20–20. 11 indexed citations
6.
Cumeras, Raquel, et al.. (2021). Comprehensive Volatilome and Metabolome Signatures of Colorectal Cancer in Urine: A Systematic Review and Meta-Analysis. Cancers. 13(11). 2534–2534. 34 indexed citations
7.
Ràfols, Pere, Bram Heijs, Óscar Yanes, et al.. (2020). rMSIproc: an R package for mass spectrometry imaging data processing. Bioinformatics. 36(11). 3618–3619. 23 indexed citations
8.
Ràfols, Pere, Sònia Torres, R. Calavia, et al.. (2018). Assessing the potential of sputtered gold nanolayers in mass spectrometry imaging for metabolomics applications. PLoS ONE. 13(12). e0208908–e0208908. 31 indexed citations
9.
Ràfols, Pere, et al.. (2018). Novel automated workflow for spectral alignment and mass calibration in MS imaging using a sputtered Ag nanolayer. Analytica Chimica Acta. 1022. 61–69. 20 indexed citations
10.
Domingo-Almenara, Xavier, J. Brezmes, Gabriela Venturini, et al.. (2017). Baitmet, a computational approach for GC–MS library-driven metabolite profiling. Metabolomics. 13(8). 6 indexed citations
11.
Domingo-Almenara, Xavier, Alexandre Perera-Lluna, Noelia Ramírez, & J. Brezmes. (2016). Automated resolution of chromatographic signals by independent component analysis–orthogonal signal deconvolution in comprehensive gas chromatography/mass spectrometry-based metabolomics. Computer Methods and Programs in Biomedicine. 130. 135–141. 10 indexed citations
12.
Domingo-Almenara, Xavier, Alexandre Perera-Lluna, & J. Brezmes. (2016). Avoiding hard chromatographic segmentation: A moving window approach for the automated resolution of gas chromatography–mass spectrometry-based metabolomics signals by multivariate methods. Journal of Chromatography A. 1474. 145–151. 6 indexed citations
13.
Domingo-Almenara, Xavier, Alexandre Perera-Lluna, Noelia Ramírez, et al.. (2015). Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation. Journal of Chromatography A. 1409. 226–233. 22 indexed citations
14.
Gómez, Josep, J. Brezmes, Roger Mallol, et al.. (2014). Dolphin: a tool for automatic targeted metabolite profiling using 1D and 2D 1H-NMR data. Analytical and Bioanalytical Chemistry. 406(30). 7967–7976. 52 indexed citations
15.
Mallol, Roger, Miguel Rodríguez, J. Brezmes, L. Masana, & Xavier Correig. (2012). Human serum/plasma lipoprotein analysis by NMR: Application to the study of diabetic dyslipidemia. Progress in Nuclear Magnetic Resonance Spectroscopy. 70. 1–24. 48 indexed citations
16.
Vinaixa, María, Miguel Rodríguez, Anna Rull, et al.. (2010). Metabolomic Assessment of the Effect of Dietary Cholesterol in the Progressive Development of Fatty Liver Disease. Journal of Proteome Research. 9(5). 2527–2538. 125 indexed citations
17.
Rull, Anna, María Vinaixa, Miguel Rodríguez, et al.. (2009). Metabolic phenotyping of genetically modified mice: An NMR metabonomic approach☆. Biochimie. 91(8). 1053–1057. 22 indexed citations
18.
Calavia, R., J. Brezmes, Radu Ionescu, & Eduard Llobet. (2006). Regression using fuzzy adaptive resonant theory neural network. Electronics Letters. 42(24). 1415–1416. 1 indexed citations
19.
Brezmes, J., et al.. (2005). Discrimination between different samples of olive oil using variable selection techniques and modified fuzzy artmap neural networks. IEEE Sensors Journal. 5(3). 463–470. 34 indexed citations
20.

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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