World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
36
Citations
8209
World Ranking
8573
National Ranking
123

Lars Malmström 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 Lars Malmström sits on this spectrum.

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 publications 804+

This scientist: 73 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

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

Lars Malmström 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 Lars Malmström sits on this spectrum.

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: 36 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Lars Malmström is affiliated with the University of Zurich in Switzerland. Their research spans fields including Biochemistry, Genetics and Molecular Biology, as well as Medicine. Within these broader areas, Malmström's work focuses on subfields such as Molecular Biology, Public Health, Environmental and Occupational Health, Spectroscopy, Infectious Diseases, and Clinical Biochemistry.

The scientist's publication record includes contributions to several prominent venues. Frequent publication venues include bioRxiv (Cold Spring Harbor Laboratory), Nature Communications, Zenodo (CERN European Organization for Nuclear Research), Frontiers in Immunology, and PLoS Computational Biology.

Recent publications by Lars Malmström, listing full titles and publication details, include:

  • "Macromolecular modeling and design in Rosetta: recent methods and frameworks," 2020, Nature Methods
  • "Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis," 2023, Nature Communications
  • "Spike-Dependent Opsonization Indicates Both Dose-Dependent Inhibition of Phagocytosis and That Non-Neutralizing Antibodies Can Confer Protection to SARS-CoV-2," 2022, Frontiers in Immunology
  • "Cerebrospinal fluid proteome maps detect pathogen-specific host response patterns in meningitis," 2021, eLife
  • "Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance," 2021, Journal of Proteome Research

The research topics covered through Malmström's work include:

  • Streptococcal Infections and Treatments
  • Advanced Proteomics Techniques and Applications
  • Metabolomics and Mass Spectrometry Studies
  • Mass Spectrometry Techniques and Applications
  • Neonatal and Maternal Infections
  • Bacterial Identification and Susceptibility Testing
  • Antimicrobial Resistance in Staphylococcus

The scientist frequently collaborates with a range of coauthors including Johan Malmström, Hamed Khakzad, Lotta Happonen, Di Tang, and Tirthankar Mohanty. The number of joint publications with these collaborators varies, reflecting ongoing research partnerships.

Best Publications

  • Macromolecular modeling and design in Rosetta: recent methods and frameworks

    Julia Koehler Leman;Brian D. Weitzner;Brian D. Weitzner;Steven M. Lewis;Steven M. Lewis;Jared Adolf-Bryfogle

  • OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data

    Hannes L Röst;George Rosenberger;Pedro Navarro;Ludovic Gillet

  • OpenMS: a flexible open-source software platform for mass spectrometry data analysis

    Hannes L Röst;Hannes L Röst;Timo Sachsenberg;Stephan Aiche;Chris Bielow

  • An objective comparison of cell-tracking algorithms

    Vladimír Ulman;Martin Maška;Klas E G Magnusson;Olaf Ronneberger

  • Structural Probing of a Protein Phosphatase 2A Network by Chemical Cross-Linking and Mass Spectrometry

    Franz Herzog;Abdullah Kahraman;Daniel Boehringer;Raymond Mak

  • Automated prediction of CASP-5 structures using the Robetta server.

    Dylan Chivian;David E. Kim;Lars Malmström;Philip Bradley

  • De Novo Prediction of Three-dimensional Structures for Major Protein Families

    Richard Bonneau;Charlie E.M Strauss;Carol A Rohl;Dylan Chivian

  • Assigning Function to Yeast Proteins by Integration of Technologies

    Tony R. Hazbun;Lars Malmström;Scott Anderson;Beth J. Graczyk

  • Prediction of CASP6 structures using automated robetta protocols

    Dylan Chivian;David E. Kim;Lars Malmström;Jack Schonbrun

  • TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics

    Hannes L Röst;Yansheng Liu;Giuseppe D'Agostino;Matteo Zanella

  • Cross-Link Guided Molecular Modeling with ROSETTA

    Abdullah Kahraman;Franz Herzog;Alexander Leitner;George Rosenberger

  • An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics

    Hendrik Weisser;Sven Nahnsen;Jonas Grossmann;Lars Nilse

  • pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library.

    Hannes L. Röst;Hannes L. Röst;Uwe Schmitt;Ruedi Aebersold;Ruedi Aebersold;Lars Malmström

  • Xwalk: computing and visualizing distances in cross-linking experiments

    Abdullah Kahraman;Lars Malmström;Ruedi Aebersold

  • openBIS: a flexible framework for managing and analyzing complex data in biology research

    Angela Bauch;Izabela Adamczyk;Piotr Buczek;Franz-Josef Elmer;Franz-Josef Elmer

  • Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS

    George Rosenberger;George Rosenberger;Yansheng Liu;Hannes L Röst;Hannes L Röst;Christina Ludwig;Christina Ludwig

  • FAIRDOMHub: a repository and collaboration environment for sharing systems biology research

    Katherine Wolstencroft;Olga Krebs;Jacky L. Snoep;Jacky L. Snoep;Natalie J. Stanford

  • Automated prediction of domain boundaries in CASP6 targets using Ginzu and RosettaDOM

    David E. Kim;Dylan Chivian;Lars Malmström;David Baker

  • A Computational Tool to Detect and Avoid Redundancy in Selected Reaction Monitoring

    Hannes Luc Rost;Hannes Luc Rost;Lars Malmstrom;Ruedi Aebersold;Ruedi Aebersold

  • DIANA - algorithmic improvements for analysis of data-independent acquisition MS data

    Johan Teleman;Hannes L Röst;George Rosenberger;Uwe Schmitt

Frequent Co-Authors

David Baker
David Baker University of Washington
György Marko-Varga
György Marko-Varga Lund University
Richard Bonneau
Richard Bonneau New York University
Anders Malmström
Anders Malmström Lund University
David R. Goodlett
David R. Goodlett University of Victoria
Trisha N. Davis
Trisha N. Davis University of Washington
Lars Björck
Lars Björck Lund University
Jacky L. Snoep
Jacky L. Snoep Stellenbosch University

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