Dr. Luo, Ruibang

Assistant Professor

Tel: (+852) 2859 2186
Fax: (+852) 2559 8447
Email: rbluo [AT] cs [DOT] hku [DOT] hk
Homepage: https://www.cs.hku.hk/~rbluo

Luo received his B.E. degree in bio-engineering, as the top graduate, from the South China University of Technology in 2010 and his Ph.D. degree in computer science from the University of Hong Kong in 2015. He was a postdoctoral fellow in the Center of Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine. Luo is a researcher working on 1) bioinformatics algorithm, 2) precision medicine, 3) metagenomics, and 4) literature mining. His research results have been published in peer-reviewed journals including Nature, Nature Biotechnology, Nature Machine Intelligence, Nature Communications, and Bioinformatics.

I'm looking for dedicated and self-motivated students and postdocs with passion in bioinformatics. If you enjoy deciphering biological and medical big data by writing up your own code to target for the best speed, sensitivity and accuracy, you might be interested in working with me. Please contact me through email with your CV.

Research Interests

1) bioinformatics algorithm, 2) precision medicine, 3) metagenomics, and 4) literature mining

Selected Publications

  • Li et al., Building a Chinese pan-genome of 486 individuals, Communications Biology 2021
  • Xie et al., The applications and potentials of nanopore sequencing in the (epi)genome and (epi)transcriptome era, The Innovation 2021
  • Luo et al., SARS‐CoV‐2 biology and variants: anticipation of viral evolution and what needs to be done, Environmental Microbiology 2021
  • Su et al., RENET2: High-Performance Full-text Gene-Disease Relation Extraction with Iterative Training Data Expansion, NAR Genomics and Bioinformatics 2021
  • Luo et al., Exploring the limit of using a deep neural network on pileup data for germline variant calling, Nature Machine Intelligence, 2020
  • Luo et al., A multi-task convolutional deep neural network for variant calling in single molecule sequencing, Nature Communications 2019
  • Luo et al., 16GT: a fast and sensitive variant caller using a 16-genotype probabilistic model, Oxford GigaScience 2017
  • Li et al., MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph, Bioinformatics 2015
  • Cao et al., De novo assembly of a haplotype-resolved human genome, Nature Biotechnology 2015
  • Xie et al., SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads, Bioinformatics 2014
  • Luo et al., SOAP3-dp: Fast, Accurate and Sensitive GPU-based Short Read Aligner, PLoS ONE 2013
  • Zhang et al., Oyster genome reveals stress adaptation and shell formation complexity, Nature 2012
  • Luo et al., SOAPdenovo2: An empirically improved memory-efficient short-read de novo assembler, Oxford GigaScience 2012
  • Li et al., Structural variation in two human genomes mapped at single-nucleotide resolution by whole genome de novo assembly, Nature Biotechnology 2011
  • Li et al. , Building the sequence map of the human pan-genome, Nature Biotechnology 2010

Recent Research Grants

  • 2021, RGC GRF, PI, "Cancer mutation detection using Single Molecule Sequencing"
  • 2021, ITF PRP, Co-PI, "Cardiovascular risk prediction model for patients on lipid modifying drugs"
  • 2021, ITF ITSP Platform Project, Co-PI, "Towards a Fully-Automated Karyotype Analysis for Detecting Chromosomal Abnormality via Intelligent Bioinformatics and Image Analysis"
  • 2021, RGC TRS, Co-I, "Assess antibiotic resistome flows from pollution hotspots to environments and explore the control strategies"
  • 2019, RGC TRS, Co-I, "Fighting Disease Recurrence and Promoting Tissue Repair after Liver Transplantation: Translating Basic Discoveries to Clinical Excellence"
  • 2018, ITF ITSP Platform Project, Co-I, Advanced 3GS-based bioinformatics algorithms and a complete bioinformatics solution for clinical genetics
  • 2018, RGC ECS, PI, An Artificial Neural Network-based discriminator for validating clinically significant genomic variants