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ERIC LU ZHANG (张璐)

Hong Kong Baptist University | Eric's Lab

Assistant Professor, Department of Computer Science

ericluzhang at comp.hkbu.edu.hk


SHORT BIO

Dr. Lu (Eric) Zhang is an Assistant Professor of Computer Science at Hong Kong
Baptist University (HKBU). Before joining HKBU, he was a postdoctoral fellow in
the Department of Computer Science and Pathology at Stanford University,
supervised by Prof. Serafim Batzoglou and Prof. Arend Sidow. He received an
MPhil degree from Li Ka Shing Faculty of Medicine at The University of Hong Kong
and a Ph.D. degree in Computer Science from City University of Hong Kong in 2012
and 2016, respectively. In 2008, he received a B.Eng in Software Engineering
from Tianjin University. In 2015, he was a visiting scholar in the Department of
Mathematics at UC Berkeley and worked with Prof. Stephen Smale. He has an
interdisciplinary background in genomics, statistics and computer science. His
primary research interests are computational genomics, bioinformatics and
machine learning. His team works specifically on 1. developing computational
tools to analyze advanced high-throughput sequencing data from metagenome and
human genome; 2. developing deep learning models to understand metagenome and
single-cell multiomics data. His recent research interest is AI for Science. His
work has been published in several top-tier journals, such as PNAS, Briefings in
Bioinformatics, NAR Genomics and Bioinformatics, GigaScience, Nature
Communications, Nature Genetics, Genome Biology, Bioinformatics, etc.


NEWS

Jun 17, 2024 LRTK got published on GigaSicence! May 31, 2024 Pangaea got
published on Nature Communication! Nov 07, 2015 A long announcement with details


SELECTED PUBLICATIONS

 1. Exploring high-quality microbial genomes by assembling short-reads with
    long-range connectivity
    Zhenmiao Zhang, Jin Xiao, Hongbo Wang, and 9 more authors
    Nature Communications, May 2024
    Publisher: Nature Publishing Group
    Abs
    
    Although long-read sequencing enables the generation of complete genomes for
    unculturable microbes, its high cost limits the widespread adoption of
    long-read sequencing in large-scale metagenomic studies. An alternative
    method is to assemble short-reads with long-range connectivity, which can be
    a cost-effective way to generate high-quality microbial genomes. Here, we
    develop Pangaea, a bioinformatic approach designed to enhance metagenome
    assembly using short-reads with long-range connectivity. Pangaea leverages
    connectivity derived from physical barcodes of linked-reads or virtual
    barcodes by aligning short-reads to long-reads. Pangaea utilizes a deep
    learning-based read binning algorithm to assemble co-barcoded reads
    exhibiting similar sequence contexts and abundances, thereby improving the
    assembly of high- and medium-abundance microbial genomes. Pangaea also
    leverages a multi-thresholding algorithm strategy to refine assembly for
    low-abundance microbes. We benchmark Pangaea on linked-reads and a
    combination of short- and long-reads from simulation data, mock communities
    and human gut metagenomes. Pangaea achieves significantly higher contig
    continuity as well as more near-complete metagenome-assembled genomes
    (NCMAGs) than the existing assemblers. Pangaea also generates three complete
    and circular NCMAGs on the human gut microbiomes.

 2. A machine learning model for disease risk prediction by integrating genetic
    and non-genetic factors
    Yu Xu, Chonghao Wang, Zeming Li, and 4 more authors
    May 2022
    
    
 3. DeepDRIM: a deep neural network to reconstruct cell-type-specific gene
    regulatory network using single-cell RNA-seq data
    Jiaxing Chen, ChinWang Cheong, Liang Lan, and 5 more authors
    May 2021
    
    Abs
    
    Single-cell RNA sequencing has enabled to capture the gene activities at
    single-cell resolution, thus allowing reconstruction of cell-type-specific
    gene regulatory networks (GRNs). The available algorithms for reconstructing
    GRNs are commonly designed for bulk RNA-seq data, and few of them are
    applicable to analyze scRNA-seq data by dealing with the dropout events and
    cellular heterogeneity. In this paper, we represent the joint gene
    expression distribution of a gene pair as an image and propose a novel
    supervised deep neural network called DeepDRIM which utilizes the image of
    the target TF-gene pair and the ones of the potential neighbors to
    reconstruct GRN from scRNA-seq data. Due to the consideration of TF-gene
    pair’s neighborhood context, DeepDRIM can effectively eliminate the false
    positives caused by transitive gene–gene interactions. We compared DeepDRIM
    with nine GRN reconstruction algorithms designed for either bulk or
    single-cell RNA-seq data. It achieves evidently better performance for the
    scRNA-seq data collected from eight cell lines. The simulated data show that
    DeepDRIM is robust to the dropout rate, the cell number and the size of the
    training data. We further applied DeepDRIM to the scRNA-seq gene expression
    of B cells from the bronchoalveolar lavage fluid of the patients with mild
    and severe coronavirus disease 2019. We focused on the cell-type-specific
    GRN alteration and observed targets of TFs that were differentially
    expressed between the two statuses to be enriched in lysosome, apoptosis,
    response to decreased oxygen level and microtubule, which had been proved to
    be associated with coronavirus infection.

Contact me by dropping me an Email: ericluzhang at hkbu.edu.hk
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