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Yasset Perez-Riverol

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Engineering and Technology 43 5974 5764 381 361 119 29623

Yasset Perez-Riverol publications per year

The chart shows the history of publications by Yasset Perez-Riverol between 2008 and 2026, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Yasset Perez-Riverol published across 19 years, from 2008 to 2026, averaging 8.8 papers a year. Output peaked at 20 publications in 2021. 16 of the 168 publications appeared in the last two years.

No. of publications
5 10 15 20
Bar chart. Horizontal axis: year, 2008 to 2026. Vertical axis: number of publications, 0 to 20. Peak 20 publications in 2021. 2008: 1 publication 2009: 0 publications 2010: 2 publications 2011: 2 publications 2012: 6 publications 2013: 7 publications 2014: 7 publications 2015: 8 publications 2016: 11 publications 2017: 13 publications 2018: 8 publications 2019: 12 publications 2020: 14 publications 2021: 20 publications 2022: 9 publications 2023: 17 publications 2024: 15 publications 2025: 15 publications 2026: 1 publication
2008 2026

168 publications in total across all disciplines

View publications per year as a table
Yasset Perez-Riverol: publications per year, 2008 to 2026
Year Publications
2008 1
2009 0
2010 2
2011 2
2012 6
2013 7
2014 7
2015 8
2016 11
2017 13
2018 8
2019 12
2020 14
2021 20
2022 9
2023 17
2024 15
2025 15
2026 1
Total 168
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Yasset Perez-Riverol publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Yasset Perez-Riverol sits on this spectrum.

No. of scientists
100 200 300 400
Bar chart with 78 bars. Horizontal axis: publications, 38–47 to 804+. Vertical axis: number of scientists, 0 to 457. Most scientists, 457, have 148–157 publications. The last bar groups every scientist with 804 publications or more. The highlighted bar, 118–127 publications, is where this scientist sits. 38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38–47 publications 804+

This scientist: 119 publications — 15th percentile

15% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

View publications distribution as a table
Number of Engineering and Technology scientists by publication count, Research.com 2026 ranking edition. Based on 9,796 ranked scientists.
Publications Scientists This scientist
38–47 20
48–57 35
58–67 96
68–77 135
78–87 190
88–97 259
98–107 283
108–117 369
118–127 341 119
128–137 386
138–147 372
148–157 457
158–167 415
168–177 407
178–187 421
188–197 378
198–207 403
208–217 317
218–227 346
228–237 321
238–247 260
248–257 280
258–267 240
268–277 214
278–287 242
288–297 203
298–307 166
308–317 154
318–327 175
328–337 159
338–347 99
348–357 131
358–367 106
368–377 118
378–387 97
388–397 108
398–407 82
408–417 71
418–427 64
428–437 55
438–447 54
448–457 60
458–467 47
468–477 40
478–487 30
488–497 29
498–507 38
508–517 40
518–527 32
528–537 23
538–547 28
548–557 23
558–567 19
568–577 16
578–587 17
588–597 18
598–607 22
608–617 15
618–627 9
628–637 11
638–647 21
648–657 12
658–667 9
668–677 11
678–687 9
688–697 6
698–707 14
708–717 7
718–727 8
728–737 10
738–747 9
748–757 5
758–767 5
768–777 11
778–787 7
788–797 2
798–803 4
804+ 100
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Yasset Perez-Riverol D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Yasset Perez-Riverol sits on this spectrum.

No. of scientists
100 200 300 400
Bar chart with 78 bars. Horizontal axis: D-Index, 30 to 107+. Vertical axis: number of scientists, 0 to 426. Most scientists, 426, have 42 D-Index. The last bar groups every scientist with 107 D-Index or more. The highlighted bar, 43 D-Index, is where this scientist sits. 30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 43 D-Index — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

View D-Index distribution as a table
Number of Engineering and Technology scientists by D-index, Research.com 2026 ranking edition. Based on 9,796 ranked scientists.
D-Index Scientists This scientist
30 59
31 114
32 129
33 189
34 200
35 262
36 311
37 312
38 350
39 385
40 348
41 362
42 426
43 380 43
44 310
45 341
46 301
47 306
48 271
49 246
50 210
51 253
52 213
53 221
54 195
55 186
56 170
57 167
58 166
59 144
60 152
61 141
62 138
63 131
64 118
65 114
66 119
67 95
68 87
69 77
70 89
71 69
72 54
73 46
74 55
75 54
76 49
77 53
78 46
79 28
80 39
81 36
82 24
83 26
84 36
85 18
86 25
87 19
88 26
89 27
90 23
91 15
92 12
93 9
94 15
95 10
96 13
97 13
98 9
99 7
100 7
101 8
102 7
103 7
104 9
105 6
106 9
107+ 99
Download as CSV

Overview

Yasset Perez-Riverol is affiliated with the European Bioinformatics Institute in the United Kingdom. Their research spans across the fields of Biochemistry, Genetics, and Molecular Biology, with significant contributions also in Chemistry. The main subfields of study include Molecular Biology, Spectroscopy, Information Systems and Management, and Epidemiology.

Their work primarily focuses on Advanced Proteomics Techniques and Applications, Metabolomics and Mass Spectrometry Studies, Mass Spectrometry Techniques and Applications, Genomics and Phylogenetic Studies, Scientific Computing and Data Management, Biomedical Text Mining and Ontologies, and Genetics, Bioinformatics, and Biomedical Research.

Frequent publication venues for their work include:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of Proteome Research
  • Nucleic Acids Research
  • Nature Methods
  • Scientific Data

Frequent coauthors collaborating with Yasset Perez-Riverol are:

  • Juan Antonio Vizcaíno
  • Eric W. Deutsch
  • Timo Sachsenberg
  • Wout Bittremieux
  • Mingze Bai

Among their recent papers are:

  • The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences (2021, Nucleic Acids Research)
  • The PRIDE database at 20 years: 2025 update (2024, Nucleic Acids Research)
  • The ProteomeXchange consortium at 10 years: 2023 update (2022, Nucleic Acids Research)
  • MaxDIA enables library-based and library-free data-independent acquisition proteomics (2021, Nature Biotechnology)
  • MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets (2020, Nature Methods)

Best Publications

  • The PRIDE database and related tools and resources in 2019: improving support for quantification data.

    Yasset Perez-Riverol;Attila Csordas;Jingwen Bai;Manuel Bernal-Llinares

  • The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences.

    Yasset Perez-Riverol;Jingwen Bai;Chakradhar Bandla;David García-Seisdedos

  • 2016 update of the PRIDE database and its related tools

    Juan Antonio Vizcaíno;Attila Csordas;Noemi Del-Toro;José A. Dianes

  • The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013.

    Juan Antonio Vizcaíno;Richard G. Côté;Attila Csordas;José A. Dianes

  • The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition

    Eric W. Deutsch;Attila Csordas;Zhi Sun;Andrew Jarnuczak

  • BioContainers: an open-source and community-driven framework for software standardization.

    Felipe da Veiga Leprevost;Björn A Grüning;Saulo Alves Aflitos;Hannes L Röst

  • The ProteomeXchange consortium in 2020: enabling 'big data' approaches in proteomics.

    Eric W. Deutsch;Nuno Bandeira;Nuno Bandeira;Vagisha Sharma;Yasset Pérez-Riverol

  • A multicenter study benchmarks software tools for label-free proteome quantification

    Pedro Navarro;Jörg Kuharev;Ludovic C. Gillet;Oliver M. Bernhardt

  • ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion.

    Niels Hulstaert;Jim Shofstahl;Timo Sachsenberg;Mathias Walzer

  • MaxDIA enables library-based and library-free data-independent acquisition proteomics

    Pavel Sinitcyn;Hamid Hamzeiy;Favio Salinas Soto;Daniel Itzhak

  • Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets

    Johannes Griss;Yasset Perez-Riverol;Steve Lewis;David L Tabb

  • PRIDE Inspector Toolsuite: Moving Toward a Universal Visualization Tool for Proteomics Data Standard Formats and Quality Assessment of ProteomeXchange Datasets

    Yasset Perez-Riverol;Qing Wei Xu;Rui Wang;Julian Uszkoreit

  • Making proteomics data accessible and reusable: Current state of proteomics databases and repositories

    Yasset Perez-Riverol;Emanuele Alpi;Rui Wang;Henning Hermjakob

  • Discovering and linking public omics data sets using the Omics Discovery Index

    Yasset Perez-Riverol;Mingze Bai;Mingze Bai;Mingze Bai;Felipe Da Veiga Leprevost;Silvano Squizzato

  • Ten Simple Rules for Taking Advantage of Git and GitHub.

    Yasset Perez-Riverol;Laurent Gatto;Rui Wang;Timo Sachsenberg

  • The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience

    Johannes Griss;Johannes Griss;Andrew R. Jones;Timo Sachsenberg;Mathias Walzer

  • Four simple recommendations to encourage best practices in research software

    Rafael C. Jiménez;Mateusz Kuzak;Monther Alhamdoosh;Michelle Barker

  • MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets.

    Meena Choi;Jeremy Carver;Cristina Chiva;Manuel Tzouros

  • PRIDE Inspector: a tool to visualize and validate MS proteomics data

    Rui Wang;Antonio Fabregat;Daniel Ríos;David Ovelleiro

  • A proteomics sample metadata representation for multiomics integration and big data analysis.

    Chengxin Dai;Anja Füllgrabe;Julianus Pfeuffer;Julianus Pfeuffer;Elizaveta M. Solovyeva;Elizaveta M. Solovyeva

  • Open source libraries and frameworks for mass spectrometry based proteomics: A developer's perspective☆

    Yasset Perez-Riverol;Rui Wang;Henning Hermjakob;Markus Müller

  • Universal Spectrum Identifier for mass spectra

    Eric W. Deutsch;Yasset Perez-Riverol;Jeremy Carver;Shin Kawano

  • PIA: An Intuitive Protein Inference Engine with a Web-Based User Interface.

    Julian Uszkoreit;Alexandra Maerkens;Yasset Perez-Riverol;Helmut E. Meyer

  • Accurate estimation of Isoelectric Point of Protein and Peptide based on Amino Acid Sequences

    Enrique Audain;Yassel Ramos;Henning Hermjakob;Darren R. Flower

  • In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics.

    Enrique Audain;Julian Uszkoreit;Timo Sachsenberg;Julianus Pfeuffer

  • Expanding the Use of Spectral Libraries in Proteomics.

    Eric W. Deutsch;Yasset Perez-Riverol;Robert J. Chalkley;Mathias Wilhelm

  • On best practices in the development of bioinformatics software.

    Felipe da Veiga Leprevost;Valmir C. Barbosa;Eduardo L. Francisco;Yasset Perez-Riverol

  • An integrated landscape of protein expression in human cancer.

    Andrew F. Jarnuczak;Hanna Najgebauer;Mitra Barzine;Deepti J. Kundu

  • Open source libraries and frameworks for biological data visualisation: a guide for developers

    Rui Wang;Yasset Perez-Riverol;Henning Hermjakob;Juan Antonio Vizcaíno

  • Isoelectric point optimization using peptide descriptors and support vector machines.

    Yasset Perez-Riverol;Enrique Audain;Aleli Millan;Yassel Ramos

  • ms-data-core-api: an open-source, metadata-oriented library for computational proteomics.

    Yasset Perez-Riverol;Julian Uszkoreit;Aniel Sanchez;Tobias Ternent

  • A Protein Standard That Emulates Homology for the Characterization of Protein Inference Algorithms.

    Fredrik Edfors;Yasset Perez-Riverol;Samuel H. Payne

Frequent Co-Authors

Juan Antonio Vizcaíno
Juan Antonio Vizcaíno European Bioinformatics Institute
Henning Hermjakob
Henning Hermjakob European Bioinformatics Institute
Eric W. Deutsch
Eric W. Deutsch University of Washington
Nuno Bandeira
Nuno Bandeira University of California, San Diego
Reza M. Salek
Reza M. Salek International Agency For Research On Cancer
Alvis Brazma
Alvis Brazma European Bioinformatics Institute
Oliver Kohlbacher
Oliver Kohlbacher University of Tübingen
Andrew R. Jones
Andrew R. Jones University of Liverpool
Alexey I. Nesvizhskii
Alexey I. Nesvizhskii University of Michigan–Ann Arbor
Robert J. Chalkley
Robert J. Chalkley University of California, San Francisco

External Links

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Best Scientists Citing Yasset Perez-Riverol

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Publications: 152

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Publications: 106

Matthias Mann

Matthias Mann

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Publications: 87

Dörte Becher

Dörte Becher

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Publications: 68

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Albert Sickmann

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Publications: 64

Lennart Martens

Lennart Martens

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Publications: 58

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Setsuko Komatsu

Fukui University of Technology

Publications: 57

Martin von Bergen

Martin von Bergen

Helmholtz Centre for Environmental Research

Publications: 54

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Bernhard Kuster

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Publications: 52

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Karl Mechtler

Research Institute of Molecular Pathology

Publications: 52

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Mayo Clinic

Publications: 52

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Steven P. Gygi

Harvard University

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Damarys Loew

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Jeroen Krijgsveld

German Cancer Research Center

Publications: 45

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