World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
5239
World Ranking
11704
National Ranking
4792

André Leier 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 André Leier 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 69 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.

André Leier 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 André Leier sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

André Leier is affiliated with the University of Alabama at Birmingham in the United States. Their research spans primarily the fields of biochemistry, genetics, and molecular biology, with additional contributions to medicine. Within these broader fields, they have specialized in molecular biology, neurology, cancer research, genetics, and microbiology.

The main topics explored in their body of work include:

  • Machine Learning in Bioinformatics
  • Genomics and Phylogenetic Studies
  • Neurofibromatosis and Schwannoma Cases
  • Biochemical and Structural Characterization
  • RNA and protein synthesis mechanisms
  • Vaccines and immunoinformatics approaches
  • Neuroblastoma Research and Treatments

Leier has published extensively, with a significant number of papers appearing in journals such as Briefings in Bioinformatics and Molecular Therapy - Nucleic Acids. Their frequent publication venues include:

  • Briefings in Bioinformatics
  • Molecular Therapy - Nucleic Acids
  • Genomics Proteomics & Bioinformatics
  • Bioinformatics
  • Nucleic Acids Research

Selected recent publications demonstrate their focus on the intersection of machine learning and bioinformatics as well as protein and genomic studies. The recent papers include:

  • Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides (2021), published in Briefings in Bioinformatics
  • Procleave: Predicting Protease-Specific Substrate Cleavage Sites by Combining Sequence and Structural Information (2020), published in Genomics Proteomics & Bioinformatics
  • PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAs (2020), published in Bioinformatics
  • DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy (2020), published in Briefings in Bioinformatics
  • Positive-unlabeled learning in bioinformatics and computational biology: a brief review (2021), published in Briefings in Bioinformatics

Collaboration has been a notable aspect of their research activity. Frequent co-authors include:

  • Jiangning Song
  • Tatiana T. Marquez-Lago
  • Fuyi Li
  • Geoffrey I. Webb
  • Robert A. Kesterson

This network of collaborators suggests interdisciplinary work within computational biology and bioinformatics, particularly leveraging machine learning approaches for biological and medical challenges. The emphasis on papers involving neural networks, hybrid frameworks, and sequence-structure integration highlights an interest in developing computational tools for understanding molecular and genomic functions.

Best Publications

  • iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences.

    Zhen Chen;Pei Zhao;Fuyi Li;André Leier

  • iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

    Zhen Chen;Pei Zhao;Fuyi Li;Tatiana T Marquez-Lago

  • Cryptography with DNA binary strands

    André Leier;Christoph Richter;Wolfgang Banzhaf;Hilmar Rauhe

  • Oscillatory Regulation of Hes1: Discrete Stochastic Delay Modelling and Simulation

    Manuel Barrio;Kevin Burrage;André Leier;Tianhai Tian

  • POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles.

    Jiawei Wang;Bingjiao Yang;Jerico Nico De Leon Revote;André Leier

  • Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

    Fuyi Li;Chen Li;Chen Li;Tatiana T Marquez-Lago;André Leier

  • A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

    Shutao Mei;Fuyi Li;André Leier;Tatiana T Marquez-Lago

  • PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy.

    Jiangning Song;Fuyi Li;Andre Leier;Tatiana Marquez-Lago

  • Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors.

    Jiawei Wang;Bingjiao Yang;André Leier;Tatiana T Marquez-Lago

  • Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides.

    Jing Xu;Fuyi Li;André Leier;Dongxu Xiang

  • Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information.

    Fuyi Li;Andre Leier;Quanzhong Liu;Yanan Wang

  • MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters.

    Meng Zhang;Fuyi Li;Tatiana T Marquez-Lago;André Leier

  • Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.

    Zhen Chen;Xuhan Liu;Fuyi Li;Chen Li;Chen Li

  • Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence.

    P. Dwight Kuo;Wolfgang Banzhaf;André Leier

  • DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites

    Fuyi Li;Jinxiang Chen;Jinxiang Chen;André Leier;Tatiana Marquez-Lago

  • Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

    Yanju Zhang;Ruopeng Xie;Jiawei Wang;André Leier

  • Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods.

    Fuyi Li;Yanan Wang;Yanan Wang;Chen Li;Chen Li;Tatiana T Marquez-Lago

  • PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection

    Jiangning Song;Huilin Wang;Jiawei Wang;André Leier

  • DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy

    Ruopeng Xie;Jiahui Li;Jiawei Wang;Wei Dai

  • Bastion3: A two-layer ensemble predictor of type III secreted effectors

    Jiawei Wang;Jiahui Li;Jiahui Li;Bingjiao Yang;Ruopeng Xie

  • PASSION: an ensemble neural network approach for identifying the binding sites of RBPs on circRNAs.

    Cangzhi Jia;Yue Bi;Jinxiang Chen;Jinxiang Chen;André Leier;André Leier

Frequent Co-Authors

Jiangning Song
Jiangning Song Monash University
Geoffrey I. Webb
Geoffrey I. Webb Monash University
Tatsuya Akutsu
Tatsuya Akutsu Kyoto University
Kuo-Chen Chou
Kuo-Chen Chou The Gordon Life Science Institute
Trevor Lithgow
Trevor Lithgow Monash University
Wolfgang Banzhaf
Wolfgang Banzhaf Michigan State University
Roger J. Daly
Roger J. Daly Monash University
Robert N. Pike
Robert N. Pike La Trobe University
George Dickson
George Dickson Royal Holloway University of London
James C. Whisstock
James C. Whisstock Monash University

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