Hugo López-Fernández

1.3k total citations
57 papers, 727 citations indexed

About

Hugo López-Fernández is a scholar working on Molecular Biology, Spectroscopy and Oncology. According to data from OpenAlex, Hugo López-Fernández has authored 57 papers receiving a total of 727 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 17 papers in Spectroscopy and 7 papers in Oncology. Recurrent topics in Hugo López-Fernández's work include Advanced Proteomics Techniques and Applications (15 papers), Genomics and Phylogenetic Studies (12 papers) and Mass Spectrometry Techniques and Applications (10 papers). Hugo López-Fernández is often cited by papers focused on Advanced Proteomics Techniques and Applications (15 papers), Genomics and Phylogenetic Studies (12 papers) and Mass Spectrometry Techniques and Applications (10 papers). Hugo López-Fernández collaborates with scholars based in Spain, Portugal and United States. Hugo López-Fernández's co-authors include Miguel Reboiro‐Jato, Florentino Fdez‐Riverola, Daniel Glez‐Peña, José Luís Capelo, Hugo M. Santos, Anália Lourenço, Joaquín Cubiella, Águeda Iglesias, Cristina P. Vieira and Jorge Vieira and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Hugo López-Fernández

51 papers receiving 708 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hugo López-Fernández Spain 13 235 131 119 103 78 57 727
Richard Röttger Denmark 13 478 2.0× 37 0.3× 143 1.2× 42 0.4× 15 0.2× 56 909
Dhammika Amaratunga United States 13 486 2.1× 47 0.4× 99 0.8× 22 0.2× 27 0.3× 49 1.0k
Yassene Mohammed Netherlands 20 547 2.3× 60 0.5× 34 0.3× 112 1.1× 403 5.2× 81 1.1k
Ondřej Vaněk Czechia 20 311 1.3× 81 0.6× 70 0.6× 42 0.4× 35 0.4× 66 1.1k
Chen‐An Tsai Taiwan 20 681 2.9× 54 0.4× 104 0.9× 48 0.5× 23 0.3× 45 1.1k
Scott Hazelhurst South Africa 21 356 1.5× 88 0.7× 309 2.6× 203 2.0× 5 0.1× 78 1.4k
Bernard de Bono United Kingdom 18 1.6k 6.6× 91 0.7× 105 0.9× 42 0.4× 76 1.0× 54 2.1k
W. Jim Zheng United States 21 624 2.7× 85 0.6× 315 2.6× 50 0.5× 15 0.2× 75 1.2k
Betsy Gregory United States 19 557 2.4× 100 0.8× 43 0.4× 87 0.8× 307 3.9× 30 1.4k

Countries citing papers authored by Hugo López-Fernández

Since Specialization
Citations

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

Fields of papers citing papers by Hugo López-Fernández

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hugo López-Fernández. 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 Hugo López-Fernández. The network helps show where Hugo López-Fernández may publish in the future.

Co-authorship network of co-authors of Hugo López-Fernández

This figure shows the co-authorship network connecting the top 25 collaborators of Hugo López-Fernández. A scholar is included among the top collaborators of Hugo López-Fernández 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 Hugo López-Fernández. Hugo López-Fernández 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.
Vieira, Jorge, et al.. (2025). Predicting Which Mitophagy Proteins Are Dysregulated in Spinocerebellar Ataxia Type 3 (SCA3) Using the Auto-p2docking Pipeline. International Journal of Molecular Sciences. 26(3). 1325–1325.
2.
Agís‐Balboa, Roberto Carlos, et al.. (2023). MyBrain-Seq: A Pipeline for MiRNA-Seq Data Analysis in Neuropsychiatric Disorders. Biomedicines. 11(4). 1230–1230. 2 indexed citations
3.
López-Fernández, Hugo, Carlos Lodeiro, Rajiv Dhir, et al.. (2023). Proteomic analysis of chromophobe renal cell carcinoma and benign renal oncocytoma biopsies reveals shared metabolic dysregulation. Clinical Proteomics. 20(1). 54–54. 1 indexed citations
4.
Piñeiro-Yáñez, Elena, Hugo López-Fernández, Santiago García‐Martín, et al.. (2023). PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data. Nucleic Acids Research. 51(W1). W411–W418. 5 indexed citations
5.
López-Fernández, Hugo, et al.. (2021). Compi: a framework for portable and reproducible pipelines. PeerJ Computer Science. 7. e593–e593. 7 indexed citations
6.
López-Fernández, Hugo, et al.. (2021). Application of miRNA-seq in neuropsychiatry: A methodological perspective. Computers in Biology and Medicine. 135. 104603–104603. 6 indexed citations
7.
López-Fernández, Hugo, Cristina P. Vieira, Pedro Ferreira, et al.. (2021). On the Identification of Clinically Relevant Bacterial Amino Acid Changes at the Whole Genome Level Using Auto-PSS-Genome. Interdisciplinary Sciences Computational Life Sciences. 13(2). 334–343. 2 indexed citations
8.
Troulé, Kevin, Hugo López-Fernández, Santiago García‐Martín, et al.. (2020). DREIMT: a drug repositioning database and prioritization tool for immunomodulation. Bioinformatics. 37(4). 578–579. 12 indexed citations
9.
Graña‐Castro, Osvaldo, et al.. (2020). Metatax: Metataxonomics with a Compi-Based Pipeline for Precision Medicine. Interdisciplinary Sciences Computational Life Sciences. 12(3). 252–257. 2 indexed citations
10.
Vieira, Jorge, et al.. (2019). ATXN1 N-terminal region explains the binding differences of wild-type and expanded forms. BMC Medical Genomics. 12(1). 145–145. 7 indexed citations
11.
López-Fernández, Hugo, et al.. (2018). Modulating the protein content of complex proteomes using acetonitrile. Talanta. 182. 333–339. 3 indexed citations
12.
Araújo, J.E., Hugo López-Fernández, Mário Diniz, et al.. (2017). Dithiothreitol-based protein equalization technology to unravel biomarkers for bladder cancer. Talanta. 180. 36–46. 10 indexed citations
13.
López-Fernández, Hugo, J.E. Araújo, Daniel Glez‐Peña, et al.. (2017). S2P: A software tool to quickly carry out reproducible biomedical research projects involving 2D-gel and MALDI-TOF MS protein data. Computer Methods and Programs in Biomedicine. 155. 1–9. 3 indexed citations
14.
Jesus, Jemmyson Romário de, Hugo M. Santos, Hugo López-Fernández, et al.. (2017). Ultrasonic-based membrane aided sample preparation of urine proteomes. Talanta. 178. 864–869. 8 indexed citations
15.
López-Fernández, Hugo. (2016). Application of data mining and artificial intelligence techniques to mass spectrometry data for knowledge discovery. SHILAP Revista de lepidopterología. 19(57). 22–25.
16.
López-Fernández, Hugo, Gustavo de Souza Pessôa, Marco Aurélio Zezzi Arruda, et al.. (2016). LA-iMageS: a software for elemental distribution bioimaging using LA–ICP–MS data. Journal of Cheminformatics. 8(1). 65–65. 34 indexed citations
17.
López-Fernández, Hugo, Hugo M. Santos, José Luís Capelo, et al.. (2015). Mass-Up: an all-in-one open software application for MALDI-TOF mass spectrometry knowledge discovery. BMC Bioinformatics. 16(1). 318–318. 85 indexed citations
18.
López-Fernández, Hugo, Miguel Reboiro‐Jato, Daniel Glez‐Peña, & Florentino Fdez‐Riverola. (2014). A comprehensive analysis about the influence of low-level preprocessing techniques on mass spectrometry data for sample classification. International Journal of Data Mining and Bioinformatics. 10(4). 455–455. 3 indexed citations
19.
Glez‐Peña, Daniel, Anália Lourenço, Hugo López-Fernández, Miguel Reboiro‐Jato, & Florentino Fdez‐Riverola. (2013). Web scraping technologies in an API world. Briefings in Bioinformatics. 15(5). 788–797. 102 indexed citations
20.
López-Fernández, Hugo, Miguel Reboiro‐Jato, Daniel Glez‐Peña, et al.. (2011). Rapid development of proteomic applications with the AIBench framework. PubMed. 8(3). 171–171. 6 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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