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Computer Science

D-Index
42
Citations
29559
World Ranking
8128
National Ranking
1072

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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