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Ph.D. Candidate |
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I am working at the intersection between Deep Learning and Bioinformatics, including: - Computational Methodology in Bioinformatics - Healthcare-Related applications of Deep Learning - Electronic Health Record Analysis - Nanopore Sequencing Data Analysis - GNN in Drug Discovery |
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I started to pursue my Ph.D. degree in the Department of Computer Science and Engineering,
The Chinese University of Hong Kong (CUHK-CSE) from Aug 2021. I'm a Member of AIH Group, advised by Prof. Yu Li. I obtained my Bachelor degree in Automation Science and Technology at Xi'an Jiaotong University in July 2021. I graduated from Hefei No.1 School, a fantastic High School in July 2014. |
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| Ph.D. in Computer Science and Engineering | 2021-2025 |
| Chinese University of Hong Kong | Hong Kong SAR | |
| B.E. in Automation Science and Technology | 2017-2021 |
| Xi'an Jiaotong University | Xi'an, Shaanxi |
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| Research Intern | SmartMore Corporation | Shenzhen, Guangdong | Jul 2020 - May 2021 |
| Research Assistant | Brown University | Providence, Rhode Island | Mar 2020 - Mar 2021 |
| Research Assistant | Xi'an Jiaotong University | Xi'an, Shaanxi | Sep 2019 - Jun 2020 |
| Research Assistant | Institute of Automation, CAS | Beijing | Aug 2019 - Feb 2020 |
| Research Assistant | National University of Singapore | Singapore | Jul 2019 - Aug 2019 |
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Jiayang Chen*, Zhihang Hu*, Siqi Sun*#, Qingxiong Tan, Yixuan Wang, Qinze Yu, Licheng Zong, Liang Hong, Jin Xiao, Irwin King Yu Li# arXiv Preprint, 2022 bibtex We propose a novel RNA foundation model (RNA-FM) that can serve as the foundational model for the field. The model takes advantage of all the 23 million non-coding RNA sequences through self-supervised learning. It's effective in downstream tasks including secondary/3D structure prediction, protein-RNA binding preference modeling, and 5' UTR-based mean ribosome loading prediction. |
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Yunxiang Li*, Yumeng Wei*, Qingxiong Tan, Licheng Zong, Yixuan Wang, Jiayang Chen, Yu Li# bioRxiv Preprint, 2022 bibtex We propose a novel deep learning method for anti-CRISPR analysis (DeepAcr), which achieves impressive performance. On both the cross-fold and cross-dataset validation, our method outperforms the previous state-of-the-art methods significantly. |
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Qinze Yu, Zhihang Dong, Xingyu Fan, Licheng Zong, Yu Li# arXiv Preprint, 2021 bibtex We develop an end-to-end hierarchical multi-label deep forest framework, HMD-AMP, to annotate AMP comprehensively. After that, it further predicts what targets the AMP can effectively kill from eleven available classes. |
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Junkang Wei*, Siyuan Chen*, Licheng Zong*, Xin Gao#, Yu Li# Briefings in Bioinformatics (IF=11.622), 2021 bibtex We give a thorough review of Protein-RNA interactions, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features, and models. |
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Wenkai Han*, Yuqi Cheng*, Jiayang Chen*, Huawen Zhong, Zhihang Hu, Siyuan Chen, Licheng Zong, Irwin King, Xin Gao#, Yu Li# Genome Biology (IF=13.540), under major revision, 2021 bibtex We present a self-supervised Contrastive LEArning framework for scRNA-seq (CLEAR) profile representation and the downstream analysis. CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events. |
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Jingwen Fu, Licheng Zong, Yinbing Li, Ke Li, Bingqian Yang, Xibei Liu 39th Chinese Control Conference (CCC), 2020 bibtex We propose a new vision system for robots, the model adaptation object detection system. Instead of using a single one to solve problems, we made use of different object detection neural networks to guide the robot in accordance with various situations, with the help of a meta neural network to allocate the object detection neural networks. |
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| Execellent Graduate | Xi'an Jiaotong University | 2021 |
| Execellent Student | Xi'an Jiaotong University | 2020 |
| First Prize | National College Robot Competition (ROBOCON) | 2019 |
| Meritorious Winner | Interdisciplinary Contest In Modeling | 2019 |
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| TA - AIST4010: Foundation of applied deep learning | Spring 2022 |
| TA - BMEG3105: Data analytics for personalized genomics and precision medicine | Fall 2021 |
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--- Apr 2022, put our RNA Foundation paper on ArXiv. --- Jan 2022, started to be the TA of AIST4010. --- Nov 2021, our RBP Review Paper has been accepted by Briefings in Bioinformatics (IF=11.622). Congratulations, Junkang! --- Sep 2021, started to be the TA of BMEG3105. --- Aug 2021, became a Ph.D. student in CUHK! --- July 2021, put our RBP Review on ArXiv. --- July 2021, put our CLEAR Paper on bioXiv. --- July 2021, graduated from Xi'an Jiaotong University! --- May 2021, decided to pursue a Ph.D. in Computer Science and Engineering at CUHK! --- Mar 2021, won the Execellent Graduate of Xi'an Jiaotong University. |
