Pablo Moreno

7.3k total citations
62 papers, 1.7k citations indexed

About

Pablo Moreno is a scholar working on Molecular Biology, Plant Science and Spectroscopy. According to data from OpenAlex, Pablo Moreno has authored 62 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 24 papers in Plant Science and 11 papers in Spectroscopy. Recurrent topics in Pablo Moreno's work include Plant Virus Research Studies (21 papers), Metabolomics and Mass Spectrometry Studies (15 papers) and Phytoplasmas and Hemiptera pathogens (10 papers). Pablo Moreno is often cited by papers focused on Plant Virus Research Studies (21 papers), Metabolomics and Mass Spectrometry Studies (15 papers) and Phytoplasmas and Hemiptera pathogens (10 papers). Pablo Moreno collaborates with scholars based in United Kingdom, Spain and Germany. Pablo Moreno's co-authors include Christoph Steinbeck, J. Guerri, Reza M. Salek, Rachel Spicer, C. N. Roistacher, Irene Papatheodorou, Daniel Cañueto, Yasset Pérez‐Riverol, Marcos Edel Martínez-Montero and José Francisco Ballester-Olmos and has published in prestigious journals such as Bioinformatics, PLoS ONE and Nature Methods.

In The Last Decade

Pablo Moreno

59 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pablo Moreno United Kingdom 20 1.0k 508 224 207 162 62 1.7k
Michele Magrane United Kingdom 14 1.7k 1.6× 192 0.4× 41 0.2× 146 0.7× 42 0.3× 21 2.2k
Michael Tognolli Switzerland 8 1.3k 1.3× 657 1.3× 42 0.2× 100 0.5× 42 0.3× 11 1.9k
Tanya Berardini United States 21 3.6k 3.5× 2.9k 5.8× 92 0.4× 76 0.4× 91 0.6× 36 4.9k
Liangjiang Wang United States 26 2.6k 2.5× 2.0k 3.9× 91 0.4× 125 0.6× 38 0.2× 64 4.0k
Pierre Vincens France 15 1.9k 1.8× 378 0.7× 48 0.2× 119 0.6× 31 0.2× 30 2.4k
Dawei Lin United States 18 1.2k 1.2× 389 0.8× 59 0.3× 82 0.4× 19 0.1× 27 1.8k
Bing Xia China 16 1.5k 1.4× 328 0.6× 367 1.6× 66 0.3× 10 0.1× 31 2.2k
Mark D’Souza United States 15 1.7k 1.6× 254 0.5× 23 0.1× 39 0.2× 27 0.2× 29 2.1k
Séverine Duvaud Switzerland 5 951 0.9× 338 0.7× 31 0.1× 97 0.5× 24 0.1× 8 1.4k
Hua Xu United States 24 979 0.9× 231 0.5× 34 0.2× 407 2.0× 20 0.1× 78 1.9k

Countries citing papers authored by Pablo Moreno

Since Specialization
Citations

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

Fields of papers citing papers by Pablo Moreno

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pablo Moreno

This figure shows the co-authorship network connecting the top 25 collaborators of Pablo Moreno. A scholar is included among the top collaborators of Pablo Moreno 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 Pablo Moreno. Pablo Moreno 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.
Jakiela, Julia, Saskia Hiltemann, Jonathan Manning, et al.. (2025). Galaxy as a gateway to bioinformatics: Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for scRNA-seq. GigaScience. 14.
2.
Álvarez‐Sierra, Daniel, María Abad, Óscar González, et al.. (2023). Thyroid cells from normal and autoimmune thyroid glands suppress T lymphocytes proliferation upon contact revealing a new regulatory inhibitory type of interaction independent of PD1/PDL1. Journal of Autoimmunity. 136. 103013–103013. 3 indexed citations
3.
4.
Moreno, Pablo, et al.. (2023). SelectBCM tool: a batch evaluation framework to select the most appropriate batch-correction methods for bulk transcriptome analysis. NAR Genomics and Bioinformatics. 5(1). 1 indexed citations
5.
Walzer, Mathias, David García‐Seisdedos, Ananth Prakash, et al.. (2022). Implementing the reuse of public DIA proteomics datasets: from the PRIDE database to Expression Atlas. Scientific Data. 9(1). 335–335. 16 indexed citations
6.
Speir, Matthew L, Aparna Bhaduri, Nikolay S. Markov, et al.. (2021). UCSC Cell Browser: visualize your single-cell data. Bioinformatics. 37(23). 4578–4580. 144 indexed citations
7.
Garg, Manik, Li Xu, Pablo Moreno, et al.. (2021). Meta-analysis of COVID-19 single-cell studies confirms eight key immune responses. Scientific Reports. 11(1). 20833–20833. 9 indexed citations
8.
Foguet, Carles, Silvia Marín, Vitaly A. Selivanov, et al.. (2019). p13CMFA: Parsimonious 13C metabolic flux analysis. PLoS Computational Biology. 15(9). e1007310–e1007310. 7 indexed citations
9.
Helfrich, Eric J. N., Reiko Ueoka, Michael Rust, et al.. (2019). Automated structure prediction of trans-acyltransferase polyketide synthase products. Nature Chemical Biology. 15(8). 813–821. 94 indexed citations
10.
Connor, Thomas M., Simon Hoer, Andrew J. Mallett, et al.. (2017). Mutations in mitochondrial DNA causing tubulointerstitial kidney disease. PLoS Genetics. 13(3). e1006620–e1006620. 40 indexed citations
11.
Su, Ya, Thomas F. Hiemstra, Yahui Yan, et al.. (2017). PDLIM5 links kidney anion exchanger 1 (kAE1) to ILK and is required for membrane targeting of kAE1. Scientific Reports. 7(1). 39701–39701. 8 indexed citations
12.
Spicer, Rachel, Reza M. Salek, Pablo Moreno, Daniel Cañueto, & Christoph Steinbeck. (2017). Navigating freely-available software tools for metabolomics analysis. Metabolomics. 13(9). 106–106. 155 indexed citations
13.
14.
Moreno, Pablo, Stephan Beisken, Bhavana Harsha, et al.. (2015). BiNChE: A web tool and library for chemical enrichment analysis based on the ChEBI ontology. BMC Bioinformatics. 16(1). 56–56. 31 indexed citations
15.
Foster, Joseph, Pablo Moreno, Antonio Fabregat, et al.. (2013). LipidHome: A Database of Theoretical Lipids Optimized for High Throughput Mass Spectrometry Lipidomics. PLoS ONE. 8(5). e61951–e61951. 60 indexed citations
16.
Jayaseelan, Kalai Vanii, et al.. (2012). Natural product-likeness score revisited: an open-source, open-data implementation. BMC Bioinformatics. 13(1). 106–106. 64 indexed citations
17.
Matos, Paula de, Nico Adams, Janna Hastings, Pablo Moreno, & Christoph Steinbeck. (2011). A Database for Chemical Proteomics: ChEBI. Methods in molecular biology. 803. 273–296. 24 indexed citations
18.
Hödar, Christian, Pablo Moreno, Alex Di Genova, et al.. (2011). Genome wide identification of Acidithiobacillus ferrooxidans (ATCC 23270) transcription factors and comparative analysis of ArsR and MerR metal regulators. BioMetals. 25(1). 75–93. 17 indexed citations
19.
Adams, Nico, Paula de Matos, Adriano Dekker, et al.. (2009). Semantic access to chemistry data with the ChEBI ontology and web services.. 1 indexed citations
20.
Gottwald, T. R., et al.. (1996). Differential Effects of Toxoptera citricida vs. Aphis gossypii on Temporal Increase and Spatial Patterns of Spread of Citrus Tristeza. International Organization of Citrus Virologists Conference Proceedings (1957-2010). 13(13). 10 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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