It is my great pleasure to work with these talented and hard-working students!
Resources
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Licheng Zong [B.E. (XJTU, Automation)]
Email: lczong21ATcse.cuhk.edu.hk
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Siyuan Chen [B.E. (UESTC, Engineering&Finance)]
Email: siyuan.chenATkaust.edu.sa
KAUST Ph.D. student, co-supervised with Prof. Xin Gao
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Wenkai Han [M.S. (KAUST, CS), B.S. (USTC, Biology, BEI Elite Class)]
Email: wenkai.hanATkaust.edu.sa
KAUST Ph.D. student, co-supervised with Prof. Xin Gao
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Zhihang Hu [B.S. (SJTU, Math&CS)]
Email: zhhu21ATcse.cuhk.edu.hk
Co-supervised with Prof. Irwin King
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Qingxiong Tan [Ph.D. (HKBU, CS), M.S. (HUST, CS), B.S. (CUMT, IE) ]
Email: csqxtanATgmail.com
2021
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Yuqi Cheng [M.S.@Cornell (Computational Biology), B.S. (CAU, Biosciences&Data Science)]
Self-supervised contrastive learning for integrative single cell RNA-seq data analysis.
W Han*, Y Cheng*, J Chen*, H Zhong, Z Hu, S Chen, L Zong, I King, X Gao#, Y Li#. Briefings in Bioinformatics(IF=13.994), 2022. Preprint.
Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis.
Y Chen*, Y Wang*, Y Chen, Y Cheng, Y Wei, Y Li, J Wang, Y Wei, TF Chan#, Y Li#. Nature Communications, 2022. Preprint.
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Shawn Chen[M.S.@Georgia Tech (ECE), B.E. (XJTU, EE)]
USPNet: unbiased organism-agnostic signal peptide predictor with deep protein language model.
S Chen, Q Tan, J Li, Y Li. Submitted. Preprint.
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Jiayang Chen[MPhil (BUT, ICE), B.E (BUT, EIE)]
Self-supervised contrastive learning for integrative single cell RNA-seq data analysis.
W Han*, Y Cheng*, J Chen*, H Zhong, Z Hu, S Chen, L Zong, I King, X Gao#, Y Li#. Briefings in Bioinformatics(IF=13.994), 2022. Preprint.
Contact-Distil: Boosting Low Homologous Protein Contact Map Prediction by Self-Supervised Distillation.
Q Wang, J Chen, Y Zhou, Y Li, L Zheng, Z Li, S Cui. AAAI-22.
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Junkang Wei [MPhil@CUHK (Biology), B.S. (SYSU, Biology) ]
Protein-RNA interaction prediction with deep learning: Structure matters.
J Wei*, S Chen*, L Zong*, X Gao#, Y Li#. Briefing in Bioinformatics(IF=13.994), 2021. Preprint.
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Liang Hong[B.S.@NJU (Kuang Yaming Honors School, Neuroscience & Artificial Intelligence)]
fastMSA: Accelerating Multiple Sequence Alignment with Dense Retrieval on Protein Language.
L Hong, S Sun, L Zheng, Q Tan, Y Li. Submitted.
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Pengfei Zhang[B.S. (USTC, CS)]
CLMB: deep contrastive learning for robust metagenomic binning.
P Zhang, Z Jiang, Y Wang, Y Li. The 26th Annual International Conference on Research in Computational Molecular Biology (RECOMB-22). Preprint
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Qinze Yu[B.E.@UESTC (Software Engineering, International Elite Class)]
HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides.
Q Yu, Z Dong, X Fan, L Zong, Y Li. Submitted. Preprint.
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Yanshuo Chen [B.S.@THU (Biosciences)]
Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis.
Y Chen*, Y Wang*, Y Chen, Y Cheng, Y Wei, Y Li, J Wang, Y Wei, TF Chan#, Y Li#. Nature Communications, 2022. Preprint.
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Xuesong Wang[M.S. (USTC&Clemson, CS), B.E (WHU, Geomatics Engineering)]
Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration.
X Wang, Z Hu, T Yu, R Wang, Y Wei, J Shu, J Ma, Yu Li. Submitted.
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Yixuan Wang[B.S.@HIT (Math)]
Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis.
Y Chen*, Y Wang*, Y Chen, Y Cheng, Y Wei, Y Li, J Wang, Y Wei, TF Chan#, Y Li#. Nature Communications, 2022. Preprint.
CLMB: deep contrastive learning for robust metagenomic binning.
P Zhang, Z Jiang, Y Wang, Y Li. The 26th Annual International Conference on Research in Computational Molecular Biology (RECOMB-22). Preprint
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Xingyu Fan[B.E.@UESTC (Software Engineering)]
HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides.
Q Yu, Z Dong, X Fan, L Zong, Y Li. Submitted. Preprint.
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Zhengyuan Jiang[B.E.@USTC (Information Science and Technology Elite Class)]
CLMB: deep contrastive learning for robust metagenomic binning.
P Zhang, Z Jiang, Y Wang, Y Li. The 26th Annual International Conference on Research in Computational Molecular Biology (RECOMB-22). Preprint
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Yongshuo Zong[B.S. (Tongji University, CS) -> Ph.D. (The University of Edinburgh, CS)]
conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics.
Y Zong, T Yu, X Wang, Y Wang, Z Hu, Y Li. Submitted.
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Yifan Deng[B.E. (WHU, EE)]
Working on protein-drug interaction. Manuscript in preparation.
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Jianing Zhang[B.S. (ZJU, Biomedical Engineering)]
Working on scRNA-seq and scATAC-seq. Manuscript in preparation.
2021
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Yumeng Wei
Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis.
Y Chen*, Y Wang*, Y Chen, Y Cheng, Y Wei, Y Li, J Wang, Y Wei, TF Chan#, Y Li#. Nature Communications, 2022. Preprint.
Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration.
X Wang, Z Hu, T Yu, R Wang, Y Wei, J Shu, J Ma, Yu Li. Submitted.
Also Working on anti-CRISPR data.
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Jin Xiao
Working web-server and database development.
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Sheng Xu
Working on single-strand break prediction.
2021
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Tingyang Yu
Professor Charles K. Kao Research Exchange Scholarship-2022 Summer
Best Project Award in Summer Research Project-2021 of CUHK
Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration.
X Wang, Z Hu, T Yu, R Wang, Y Wei, J Shu, J Ma, Yu Li. Submitted.
Working on two more projects.
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Jiuming Wang
Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis.
Y Chen*, Y Wang*, Y Chen, Y Cheng, Y Wei, Y Li, J Wang, Y Wei, TF Chan#, Y Li#. Nature Communications, 2022. Preprint.
Professor Charles K. Kao Research Exchange Scholarship-2022 Summer
Working on scHi-C data.
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Jingchen Li
USPNet: unbiased organism-agnostic signal peptide predictor with deep protein language model.
S Chen, Q Tan, J Li, Y Li. Submitted. Preprint.
Working on one more projects.
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Yunxiang Li
Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis.
Y Chen*, Y Wang*, Y Chen, Y Cheng, Y Wei, Y Li, J Wang, Y Wei, TF Chan#, Y Li#. Nature Communications, 2022. Preprint.
Also Working on anti-CRISPR data.
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Ruijie Wang
Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration.
X Wang, Z Hu, T Yu, R Wang, Y Wei, J Shu, J Ma, Yu Li. Submitted.
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Jialiang Guo & Zihao Huang
Worked on AMR prediction. Manuscript under preparation.
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Myratgeldi Jumageldiyev & Manuchehr Tursunov
FYP students. Working on RNA SS prediction.
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Ching Fung Yeung
FYP students. Working on single-cell data.
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Zechen Li
FYP students. Working on single-cell data.
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Zhenxi Wang
Working on sequence embedding.
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Hanqun Cao
Working on generative model.
In KAUST (During Ph.D. study, before 2020)
Master students
- Siyuan Chen
Lunar Features Detection for Energy Discovery via Deep Learning.
S Chen*, Y Li*, T Zhang, X Zhu, S Sun, X Gao. Applied Energy (IF=8.558), accepted.
Two more manuscripts under preparation.
- Wenkai Han
HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes.
Y Li*, Z Xu*, W Han*, H Cao, R Umarov, A Yan, M Fan, H Chen, L Li, P Ho, X Gao. Microbiome (IF=11.607), 2021.
- Rawan Albakri
Visiting students
- Siyuan Chen, 2019, from UESTC
Lunar Features Detection for Energy Discovery via Deep Learning.
S Chen*, Y Li*, T Zhang, X Zhu, S Sun, X Gao. Applied Energy (IF=8.558), accepted.
- Noura AlRasheed, Summer, 2019, KGSP student from UCSD
- Longxi Zhou, Summer, 2018, from USTC
- Ammar Alqatari , Summer, 2018, KGSP student from Stanford
He won the first place in KGSP Poster Competition in 2018.
- Zhongxiao Li, Spring, 2018, from SUSTech
SupportNet: solving catastrophic forgetting in class incremental learning with support data.
Y Li, Z Li, L Ding, X Gao , arxiv.org/abs/1806.02942.
Deep learning in bioinformatics: introduction, application, and perspective in big data era.
Y Li, C Huang, L Ding, Z Li, Y Pan, X Gao. Methods, 2019.
[PDF]
[Code]
Cover article of the Methods issue: Deep Learning in Bioinformatics
- Zhenzhen Zou, 2018, from CAS
mlDEEPre: Multi-functional enzyme function prediction with hierarchical multi-label deep learning.
Z Zou, S Tian, X Gao, Y Li#. Frontiers in Genetics, 2019.
[PDF]
[Server]