World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
21446
World Ranking
7741
National Ranking
3343

Brendan MacLean publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Brendan MacLean sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 62 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Brendan MacLean D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Brendan MacLean sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Brendan MacLean is affiliated with the University of Washington in the United States. Their research primarily spans the fields of Chemistry and Biochemistry, Genetics, and Molecular Biology, with a focused contribution to subfields including Spectroscopy, Molecular Biology, Information Systems and Management, Information Systems, and Ecology.

The scientist's work is centered around several main topics such as Advanced Proteomics Techniques and Applications, Mass Spectrometry Techniques and Applications, and Metabolomics and Mass Spectrometry Studies. Additional areas of involvement include Scientific Computing and Data Management, Research Data Management Practices, Analytical Chemistry and Chromatography, and Machine Learning in Bioinformatics.

Brendan MacLean has been published extensively in a range of scientific journals. Frequent publication venues comprise the Journal of Proteome Research, bioRxiv (Cold Spring Harbor Laboratory), Nature Communications, Nucleic Acids Research, and Nature Methods.

Their recent papers include:

  • The ProteomeXchange consortium at 10 years: 2023 update (2022, Nucleic Acids Research)
  • Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics (2020, Journal of Proteome Research)
  • Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition (2023, Journal of Proteome Research)
  • MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale (2023, Journal of Proteome Research)
  • LipidCreator workbench to probe the lipidomic landscape (2020, Nature Communications)

Their collaborative network includes frequent co-authors such as Michael J. MacCoss, Nicholas Shulman, Michael Riffle, Brian Pratt, and Vagisha Sharma.

Best Publications

  • Skyline: an open source document editor for creating and analyzing targeted proteomics experiments

    Brendan MacLean;Daniela M. Tomazela;Nicholas Shulman;Matthew Chambers

  • A cross-platform toolkit for mass spectrometry and proteomics

    Matthew C Chambers;Brendan Maclean;Robert Burke;Dario Amodei

  • MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments

    Meena Choi;Ching-Yun Chang;Timothy Clough;Daniel Broudy

  • Using iRT, a normalized retention time for more targeted measurement of peptides

    Claudia Escher;Lukas Reiter;Brendan MacLean;Reto Ossola

  • The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics.

    Lindsay K. Pino;Brian C. Searle;James G. Bollinger;Brook Nunn

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

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

  • Targeted Peptide Measurements in Biology and Medicine: Best Practices for Mass Spectrometry-based Assay Development Using a Fit-for-Purpose Approach

    Steven A. Carr;Susan E. Abbatiello;Bradley L. Ackermann;Christoph Borchers

  • Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics

    Kendra J. Adams;Brian Pratt;Neelanjan Bose;Laura G. Dubois

  • Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry

    Brian C. Searle;Lindsay K. Pino;Jarrett D. Egertson;Ying S. Ting

  • Platform-independent and Label-free Quantitation of Proteomic Data Using MS1 Extracted Ion Chromatograms in Skyline APPLICATION TO PROTEIN ACETYLATION AND PHOSPHORYLATION

    Birgit Schilling;Matthew J. Rardin;Brendan X. MacLean;Anna M. Zawadzka

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

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

  • Building high-quality assay libraries for targeted analysis of SWATH MS data

    Olga T. Schubert;Ludovic C. Gillet;Ben C. Collins;Pedro Navarro

  • Multiplexed MS/MS for improved data-independent acquisition

    Jarrett D Egertson;Andreas Kuehn;Gennifer E Merrihew;Nicholas W Bateman

  • CPTAC Assay Portal: a repository of targeted proteomic assays

    Jeffrey R. Whiteaker;Goran N. Halusa;Andrew N. Hoofnagle;Vagisha Sharma

  • Panorama Public: A Public Repository for Quantitative Data Sets Processed in Skyline.

    Vagisha Sharma;Josh Eckels;Birgit Schilling;Christina Ludwig

  • General framework for developing and evaluating database scoring algorithms using the TANDEM search engine

    Brendan Maclean;Jimmy K. Eng;Ronald C. Beavis;Martin Mcintosh

  • Multiplexed peptide analysis using data-independent acquisition and Skyline

    Jarrett D Egertson;Brendan MacLean;Richard Johnson;Yue Xuan

  • Panorama: a targeted proteomics knowledge base.

    Vagisha Sharma;Josh Eckels;Greg K. Taylor;Nicholas J. Shulman

  • Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses

    George Rosenberger;George Rosenberger;Isabell Bludau;Isabell Bludau;Uwe Schmitt;Moritz Heusel;Moritz Heusel

  • Computational Proteomics Analysis System (CPAS): an extensible, open-source analytic system for evaluating and publishing proteomic data and high throughput biological experiments.

    Adam Rauch;Matthew Bellew;Jimmy Eng;Matthew Fitzgibbon

  • Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data

    Ying Sonia Ting;Jarrett D. Egertson;Samuel H. Payne;Sangtae Kim

Frequent Co-Authors

Michael J. MacCoss
Michael J. MacCoss University of Washington
Birgit Schilling
Birgit Schilling Buck Institute for Research on Aging
Steven A. Carr
Steven A. Carr Broad Institute
daniel c liebler
daniel c liebler Vanderbilt University
Bradford W. Gibson
Bradford W. Gibson Buck Institute for Research on Aging
Amanda G. Paulovich
Amanda G. Paulovich Fred Hutchinson Cancer Research Center
Henry Rodriguez
Henry Rodriguez National Institutes of Health
Christoph H. Borchers
Christoph H. Borchers McGill University
Thomas A. Neubert
Thomas A. Neubert New York University

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