World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
29559
World Ranking
8127
National Ranking
1072

Tak-Wah Lam 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 Tak-Wah Lam 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: 238 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

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

Tak-Wah Lam 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 Tak-Wah Lam 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Tak-Wah Lam is affiliated with the University of Hong Kong in China and has a research focus spanning the fields of Biochemistry, Genetics, and Molecular Biology as well as Medicine. Their work includes significant contributions in subfields such as Molecular Biology, Genetics, Artificial Intelligence, Cardiology and Cardiovascular Medicine, and Plant Science.

The scientist's research topics cover a broad range of genomic and molecular biology areas, including:

  • Genomics and Phylogenetic Studies
  • Genomics and Rare Diseases
  • Chromosomal and Genetic Variations
  • Biomedical Text Mining and Ontologies
  • Genomic variations and chromosomal abnormalities
  • Cancer Genomics and Diagnostics
  • Molecular Biology Techniques and Applications

Tak-Wah Lam has contributed to multiple recent scientific papers, such as:

  • "Symphonizing pileup and full-alignment for deep learning-based long-read variant calling" (2022, Nature Computational Science)
  • "Exploring the limit of using a deep neural network on pileup data for germline variant calling" (2020, Nature Machine Intelligence)
  • "Symphonizing pileup and full-alignment for deep learning-based long-read variant calling" (2021, bioRxiv, Cold Spring Harbor Laboratory)
  • "Evaluation of Mycobacterium tuberculosis enrichment in metagenomic samples using ONT adaptive sequencing and amplicon sequencing for identification and variant calling" (2023, Scientific Reports)
  • "Clair3-trio: high-performance Nanopore long-read variant calling in family trios with trio-to-trio deep neural networks" (2022, Briefings in Bioinformatics)

The researcher collaborates frequently with several colleagues, including Ruibang Luo, Zhenxian Zheng, Junhao Su, Shumin Li, and Amy Wing-Sze Leung.

Tak-Wah Lam's work is published predominantly in venues such as bioRxiv (Cold Spring Harbor Laboratory), Scientific Reports, Briefings in Bioinformatics, NAR Genomics and Bioinformatics, and BMC Bioinformatics.

Best Publications

  • MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph

    Dinghua Li;Chi-Man Liu;Ruibang Luo;Kunihiko Sadakane

  • SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler

    Ruibang Luo;Binghang Liu;Yinlong Xie;Yinlong Xie;Zhenyu Li

  • SOAP2: an improved ultrafast tool for short read alignment.

    Ruiqiang Li;Chang Yu;Yingrui Li;Tak Wah Lam

  • MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices

    Dinghua Li;Ruibang Luo;Chi-Man Liu;Chi-Ming Leung

  • SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads

    Yinlong Xie;Yinlong Xie;Gengxiong Wu;Jingbo Tang;Ruibang Luo

  • Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

    Keith R. Bradnam;Joseph N. Fass;Anton Alexandrov;Paul Baranay

  • Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

    Keith R. Bradnam;Joseph N. Fass;Anton Alexandrov;Paul Baranay

  • SOAP3-dp: Fast, Accurate and Sensitive GPU-based Short Read Aligner

    Ruibang Luo;Thomas Kf Wong;Jianqiao Zhu;Jianqiao Zhu;Chi-Man Liu

  • SOAP3: ultra-fast GPU-based parallel alignment tool for short reads.

    Chi-Man Liu;Thomas K. F. Wong;Edward Wu;Ruibang Luo

  • Erratum to "SOAPdenovo2: An empirically improved memory-efficient short-read de novo assembler" [GigaScience, (2012), 1, 18]

    Ruibang Luo;Binghang Liu;Yinlong Xie;Zhenyu Li

  • COPE: an accurate k-mer-based pair-end reads connection tool to facilitate genome assembly.

    Binghang Liu;Jianying Yuan;Siu-Ming Yiu;Zhenyu Li

  • A multi-task convolutional deep neural network for variant calling in single molecule sequencing

    Ruibang Luo;Ruibang Luo;Fritz J. Sedlazeck;Tak-Wah Lam;Michael C. Schatz

  • Compressed indexing and local alignment of DNA

    T. W. Lam;W. K. Sung;S. L. Tam;C. K. Wong

  • SOAPsplice: Genome-Wide ab initio Detection of Splice Junctions from RNA-Seq Data

    Songbo Huang;Songbo Huang;Jinbo Zhang;Ruiqiang Li;Wenqian Zhang

  • Exploring the limit of using a deep neural network on pileup data for germline variant calling

    Ruibang Luo;Chak-Lim Wong;Yat-Sing Wong;Chi-Ian Tang

  • Filtering of ineffective siRNAs and improved siRNA design tool

    Prudence W. H. Wong;T. W. Lam;Y. C. Mui;S. M. Yiu

  • High Throughput Short Read Alignment via Bi-directional BWT

    T. W. Lam;Ruiqiang Li;Alan Tam;Simon Wong

  • Energy efficient online deadline scheduling

    Ho-Leung Chan;Wun-Tat Chan;Tak-Wah Lam;Lap-Kei Lee

  • Compressed indexes for dynamic text collections

    Ho-Leung Chan;Wing-Kai Hon;Tak-Wah Lam;Kunihiko Sadakane

  • Scheduling for Speed Bounded Processors

    Nikhil Bansal;Ho-Leung Chan;Tak-Wah Lam;Lap-Kei Lee

Frequent Co-Authors

Siu-Ming Yiu
Siu-Ming Yiu University of Hong Kong
Wing-Kin Sung
Wing-Kin Sung Chinese University of Hong Kong
Ming-Yang Kao
Ming-Yang Kao Northwestern University
Kunihiko Sadakane
Kunihiko Sadakane University of Tokyo
David W. Cheung
David W. Cheung University of Hong Kong
Ruiqiang Li
Ruiqiang Li Novogene (China)
Guojie Zhang
Guojie Zhang Zhejiang University
Michael C. Schatz
Michael C. Schatz Johns Hopkins University
Daniel S. Rokhsar
Daniel S. Rokhsar University of California, Berkeley
Gane Ka-Shu Wong
Gane Ka-Shu Wong University of Alberta

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