Fábio Madeira

24.9k total citations · 3 hit papers
14 papers, 5.4k citations indexed

About

Fábio Madeira is a scholar working on Molecular Biology, Pharmacology and Information Systems and Management. According to data from OpenAlex, Fábio Madeira has authored 14 papers receiving a total of 5.4k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 2 papers in Pharmacology and 2 papers in Information Systems and Management. Recurrent topics in Fábio Madeira's work include Genomics and Phylogenetic Studies (6 papers), RNA and protein synthesis mechanisms (3 papers) and 14-3-3 protein interactions (3 papers). Fábio Madeira is often cited by papers focused on Genomics and Phylogenetic Studies (6 papers), RNA and protein synthesis mechanisms (3 papers) and 14-3-3 protein interactions (3 papers). Fábio Madeira collaborates with scholars based in United Kingdom, Czechia and Russia. Fábio Madeira's co-authors include Rodrigo López, Adrian R. Tivey, Nandana Madhusoodanan, Joon Lee, ROBERT FINN, Tamer Gür, Nicola Buso, Young Mi Park, Simon Potter and Matt Pearce and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Fábio Madeira

14 papers receiving 5.3k citations

Hit Papers

The EMBL-EBI search and s... 2019 2026 2021 2023 2019 2022 2024 1000 2.0k 3.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. The EMBL-EBI search and sequence analysis tools APIs in 2019
    2019 3.1k citations Fábio Madeira, Young Mi Park et al. Nucleic Acids Research profile →
  2. Search and sequence analysis tools services from EMBL-EBI in 2022
    2022 1.5k citations Fábio Madeira, Matt Pearce et al. Nucleic Acids Research profile →
  3. The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024
    2024 491 citations Fábio Madeira, Nandana Madhusoodanan et al. Nucleic Acids Research profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Fábio Madeira 3.2k 916 600 573 552 14 5.4k
Nicola Buso 3.1k 1.0× 944 1.0× 594 1.0× 582 1.0× 558 1.0× 5 5.2k
Nandana Madhusoodanan 3.0k 0.9× 898 1.0× 583 1.0× 545 1.0× 546 1.0× 5 5.0k
Adrian R. Tivey 3.2k 1.0× 1.0k 1.1× 628 1.0× 677 1.2× 605 1.1× 7 5.5k
Yoshitaka Moriwaki 3.7k 1.1× 649 0.7× 615 1.0× 494 0.9× 334 0.6× 32 5.5k
Stuart J. Cordwell 4.0k 1.2× 625 0.7× 650 1.1× 795 1.4× 544 1.0× 137 6.7k
Tamer Gür 2.3k 0.7× 743 0.8× 419 0.7× 442 0.8× 438 0.8× 6 3.9k
Konstantin Schütze 3.5k 1.1× 536 0.6× 590 1.0× 485 0.8× 300 0.5× 9 5.1k
Ambrish Roy 5.2k 1.6× 758 0.8× 658 1.1× 430 0.8× 573 1.0× 21 7.5k
Young Mi Park 4.4k 1.4× 1.5k 1.7× 753 1.3× 801 1.4× 633 1.1× 30 7.3k
Thomas Nordahl Petersen 3.0k 0.9× 1.1k 1.2× 490 0.8× 733 1.3× 341 0.6× 34 5.3k

Countries citing papers authored by Fábio Madeira

Since Specialization
Citations

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

Fields of papers citing papers by Fábio Madeira

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fábio Madeira

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

All Works

14 of 14 papers shown
1.
Madeira, Fábio, Nandana Madhusoodanan, Joon Lee, et al.. (2024). The EMBL-EBI Job Dispatcher sequence analysis tools framework in 2024. Nucleic Acids Research. 52(W1). W521–W525. 491 indexed citations breakdown →
2.
MacGowan, Stuart A., Fábio Madeira, Thiago Britto‐Borges, & Geoffrey J. Barton. (2024). A unified analysis of evolutionary and population constraint in protein domains highlights structural features and pathogenic sites. Communications Biology. 7(1). 447–447. 3 indexed citations
3.
Madeira, Fábio, Nandana Madhusoodanan, Joon Lee, et al.. (2024). Using EMBL‐EBI Services via Web Interface and Programmatically via Web Services. Current Protocols. 4(6). e1065–e1065. 8 indexed citations
4.
Madeira, Fábio, Matt Pearce, Adrian R. Tivey, et al.. (2022). Search and sequence analysis tools services from EMBL-EBI in 2022. Nucleic Acids Research. 50(W1). W276–W279. 1462 indexed citations breakdown →
5.
Sweeney, Blake, David Hoksza, Eric P. Nawrocki, et al.. (2021). R2DT is a framework for predicting and visualising RNA secondary structure using templates. Nature Communications. 12(1). 3494–3494. 77 indexed citations
6.
MacGowan, Stuart A., et al.. (2019). The Dundee Resource for Sequence Analysis and Structure Prediction. Protein Science. 29(1). 277–297. 13 indexed citations
7.
Madeira, Fábio, Nandana Madhusoodanan, Joon Lee, Adrian R. Tivey, & Rodrigo López. (2019). Using EMBL‐EBI Services via Web Interface and Programmatically via Web Services. Current Protocols in Bioinformatics. 66(1). e74–e74. 26 indexed citations
8.
Madeira, Fábio, Young Mi Park, Joon Lee, et al.. (2019). The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Research. 47(W1). W636–W641. 3055 indexed citations breakdown →
9.
Sallou, Olivier, Pablo Moreno, Felipe da Veiga Leprevost, et al.. (2019). Recommendations for the packaging and containerizing of bioinformatics software [version 2; peer review: 2 approved, 1 approved with reservations]. 1 indexed citations
10.
Troshin, Peter V, James B Procter, Alexander Sherstnev, et al.. (2018). JABAWS 2.2 distributed web services for Bioinformatics: protein disorder, conservation and RNA secondary structure. Bioinformatics. 34(11). 1939–1940. 41 indexed citations
11.
Madeira, Fábio, Michele Tinti, Gavuthami Murugesan, et al.. (2015). 14-3-3-Pred: improved methods to predict 14-3-3-binding phosphopeptides. Bioinformatics. 31(14). 2276–2283. 156 indexed citations
12.
Madeira, Fábio. (2014). PYCOEVOL: A Python workflow to study protein-protein coevolution. Figshare. 143–149. 2 indexed citations
13.
Tinti, Michele, Fábio Madeira, Gavuthami Murugesan, et al.. (2014). ANIA: ANnotation and Integrated Analysis of the 14-3-3 interactome. Database. 2014. bat085–bat085. 50 indexed citations
14.
Madeira, Fábio & Ludwig Krippahl. (2012). PYCOEVOL - A Python Workflow to Study Protein-protein Coevolution. 143–149. 1 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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