World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
10981
World Ranking
9979
National Ranking
621

Liam J. McGuffin 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 Liam J. McGuffin 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: 72 publications — 2nd percentile

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

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

Liam J. McGuffin 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 Liam J. McGuffin 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Liam J. McGuffin is affiliated with the University of Reading in the United Kingdom. Their research primarily focuses on the field of Biochemistry, Genetics, and Molecular Biology, with a particular emphasis on Molecular Biology. The scientist's scholarly output includes a notable number of publications related to protein structures and functions, machine learning applications in bioinformatics, and microbial metabolic engineering.

The main topics covered in their work include:

  • Protein Structure and Dynamics
  • Enzyme Structure and Function
  • Microbial Metabolic Engineering and Bioproduction
  • Machine Learning in Bioinformatics
  • Bioinformatics and Genomic Networks
  • Computational Drug Discovery Methods
  • RNA and protein synthesis mechanisms

McGuffin has contributed research published in several scientific venues, with frequent contributions to:

  • Methods in Molecular Biology
  • Nucleic Acids Research
  • Proteins Structure Function and Bioinformatics
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Cell Communication and Signaling

Recent research papers authored or co-authored by McGuffin include:

  • ModFOLD8: accurate global and local quality estimates for 3D protein models, 2021, Nucleic Acids Research
  • Prediction of protein structures, functions and interactions using the IntFOLD7, MultiFOLD and ModFOLDdock servers, 2023, Nucleic Acids Research

Frequent co-authors working alongside McGuffin include:

  • Recep Adiyaman
  • Nicholas S. Edmunds
  • Ahmet G Genc
  • Shuaa M. A. Alharbi
  • Jianlin Cheng

The range and focus of McGuffin's published work reflect an integration of computational methods with biochemical and molecular biology techniques. Their ongoing association with the University of Reading situates them within a research environment contributing to advances in understanding protein modeling, structure prediction, and bioinformatics tool development.

Best Publications

  • The PSIPRED protein structure prediction server.

    Liam J. McGuffin;Kevin Bryson;David T. Jones

  • Protein structure prediction servers at University College London

    Kevin Bryson;Liam J. McGuffin;Russell L. Marsden;Jonathan J. Ward

  • The DISOPRED server for the prediction of protein disorder

    Jonathan J. Ward;Liam J. Mcguffin;Kevin Bryson;Bernard F. Buxton

  • The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

    Naihui Zhou;Yuxiang Jiang;Timothy R. Bergquist;Alexandra J. Lee

  • Improvement of the GenTHREADER method for genomic fold recognition

    Liam J. McGuffin;David T. Jones

  • Secondary structure prediction with support vector machines.

    Jonathan J. Ward;Liam J. McGuffin;Bernard F. Buxton;David T. Jones

  • Rapid protein domain assignment from amino acid sequence using predicted secondary structure

    Russell L. Marsden;Liam J. McGuffin;David T. Jones

  • Predicting Metal-binding Site Residues in Low-resolution Structural Models

    Jaspreet Singh Sodhi;Kevin Bryson;Liam J. McGuffin;Jonathan J. Ward

  • The ModFOLD server for the quality assessment of protein structural models

    Liam J. McGuffin

  • Intrinsic disorder prediction from the analysis of multiple protein fold recognition models

    Liam J. McGuffin

  • The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction

    Daniel B. Roche;Maria T. Buenavista;Stuart J. Tetchner;Liam J. McGuffin

  • IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences

    Liam J. McGuffin;Jennifer D. Atkins;Bajuna R. Salehe;Ahmad N. Shuid

  • The ModFOLD4 server for the quality assessment of 3D protein models

    Liam J. McGuffin;Maria T. Buenavista;Daniel B. Roche

  • IntFOLD: an integrated web resource for high performance protein structure and function prediction.

    Liam J McGuffin;Recep Adiyaman;Ali H A Maghrabi;Ahmad N Shuid

  • ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models.

    Ali H. A. Maghrabi;Liam J. McGuffin

  • Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments

    Liam J. McGuffin;Daniel B. Roche

  • Benchmarking consensus model quality assessment for protein fold recognition

    Liam J McGuffin

  • Assembling novel protein folds from super-secondary structural fragments.

    David T. Jones;Liam J. McGuffin

  • Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.

    Daniel Barry Roche;Danielle Allison Brackenridge;Liam James McGuffin

  • Prediction of novel and analogous folds using fragment assembly and fold recognition

    D. T. Jones;K. Bryson;A. Coleman;Liam J. McGuffin

  • Estimation of model accuracy in CASP13.

    Jianlin Cheng;Myong‐Ho Choe;Arne Elofsson;Kun‐Sop Han

  • Improving sequence-based fold recognition by using 3D model quality assessment

    Chris S. Pettitt;Liam J. Mcguffin;David T. Jones

Frequent Co-Authors

David T. Jones
David T. Jones University College London
Christophe Dessimoz
Christophe Dessimoz University College London
Patrick A. Lewis
Patrick A. Lewis University College London
Jianlin Cheng
Jianlin Cheng University of Missouri
Tapio Salakoski
Tapio Salakoski University of Turku
Daisuke Kihara
Daisuke Kihara Purdue University West Lafayette
Peter H. Sugden
Peter H. Sugden University of Reading
Arne Elofsson
Arne Elofsson Science for Life Laboratory
Yang Zhang
Yang Zhang University of Michigan–Ann Arbor
Angela Clerk
Angela Clerk University of Reading

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