I am a Computational Biology Ph.D. student at Tsinghua University, advised by Prof. Qiangfeng Cliff Zhang. I am broadly interested in deep learning, with data-driven algorithm design and applications in real world (2D/3D computer vision and graphics) and micro world (Electron Cryo-Microscopy Density map based Molecular Structure Determination, Single Cell Clustering and RNA Structure Understanding). My recent research focuses on designing algorithms and deep learning models to gain biological insights from advances in the large data sets.

Prior to Ph.D. student period, I was a visiting scholar at the Chinese University of Hong Kong, collaborating with awesome folks in the lab of Xiaogang Wang. I also works closely with Zhe Wang, Jianping Shi and Hongsheng Li, focusing on 3D Object Detection and Molecular structure model building.

I received master and bachelor degree in Computer Science in Tianjin Polytechnic University in 2016 and 2013, respectively. I have also spent time at Multimedia Information Processing Lab, Peking University as a research intern.

Feel free to say hi: xuk16 at mails.tsinghua.edu.cn


What's New

[Jan 2021] One paper accepted to Science. Proud to apply our improved A2-Net to identify new proteins in Spliceosome, congratulations to Rui Bai and Ruixue Wan.
[Jan 2021] Our PrismNet method paper accepted to Cell Research, congratulations to all the team members.
[Nov 2020] Our PrismNet Prediction on SARS-CoV-2 paper accepted to Cell, congratulations to Lei Sun, Panpan and Wenze.
[Oct 2020] One paper accepted to Nucleic Acids Research, congratulations to Panpan and Xiaolin.
[Jul 2020] One paper accepted to Bioinformatics.
[Jul 2020] Proud to apply our PrismNet to predict host cell target proteins bind to SARS-CoV-2 RNA! Paper out on BioRxiv.
[May 2020] PrismNet method paper out on BioRxiv: Predicting dynamic cellular protein-RNA interactions using deep learning and in vivo RNA structure, code are available at Github.
[Jan 2020] One paper accepted to ICRA 2020.
[Oct 2019] Paper out on Nature Communications: SCALE method for single-cell ATAC-seq analysis via latent feature extraction, code are available at SCALE.
[Mar 2019] Paper out on BioRxiv: VRmol: an Integrative Cloud-Based Virtual Reality System to Explore Macromolecular Structure, code are available at VRmol.
[Jan 2019] Presented our work A2-Net at AAAI 2019, Hawaii.


Research

In vivo structural characterization of the whole SARS-CoV-2 RNA genome identifies host cell target proteins vulnerable to re-purposed drugs
Lei Sun*, Pan Li*, Xiaohui Ju*, Jian Rao*, Wenze Huang, Shaojun Zhang, Tuanlin Xiong, Kui Xu, Xiaolin Zhou, Lili Ren, Qiang Ding, Jianwei Wang and Qiangfeng Cliff Zhang
In vivo structural characterization of the whole SARS-CoV-2 RNA genome identifies host cell target proteins vulnerable
                    to re-purposed drugs

Predicting dynamic cellular protein-RNA interactions using deep learning and in vivo RNA structure
Lei Sun*, Kui Xu*, Wenze Huang*, Yucheng T. Yang*, Pan Li, Lei Tang, Tuanlin Xiong and Qiangfeng Cliff Zhang
PrismNet

Structure of the activated human minor spliceosome
Rui Bai*, Ruixue Wan*, Lin Wang, Kui Xu, Qiangfeng Zhang, Jianlin Lei, Yigong Shi

RASP: an atlas of transcriptome-wide RNA secondary structure probing data
Pan Li*, Xiaolin Zhou*, Kui Xu, and Qiangfeng Cliff Zhang

VRmol: an Integrative Web-Based Virtual Reality System to Explore Macromolecular Structure
Kui Xu*, Nan Liu*, Jingle Xu, Chunlong Guo, Lingyun Zhao, Hong-Wei Wang and Qiangfeng Cliff Zhang
VRmol

SCALE method for single-cell ATAC-seq analysis via latent feature extraction
Lei Xiong, Kui Xu, Kang Tian, Yanqiu Shao, Lei Tang, Ge Gao, Michael Zhang, Tao Jiang and Qiangfeng Cliff Zhang
SCALE

A2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes
Kui Xu, Zhe Wang, Jianping Shi, Hongsheng Li and Qiangfeng Cliff Zhang
A^2-Net

SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud
Hongwei Yi, Shaoshuai Shi, Mingyu Ding, Jiankai Sun, Kui Xu, Hui Zhou, Zhe Wang, Sheng Li and Guoping Wang

Bioactive functionalized monolayer graphene for high-resolution cryo-electron microscopy
Nan Liu, Jincan Zhang, Yanan Chen, Chuan Liu, Xing Zhang, Kui Xu, Jie Wen, Zhipu Luo, Shulin Chen, Peng Gao, Kaicheng Jia, Zhongfan Liu, Hailin Peng and Hong-Wei Wang

RISE: a database of RNA interactome from sequencing experiments
Jing Gong*, Di Shao*, Kui Xu, Zhipeng Lu, Zhi John Lu, Yucheng T. Yang and Qiangfeng Cliff Zhang

Reweighted sparse subspace clustering
Jun Xu, Kui Xu, Ke Chen and Jishou Ruan

Open Source

PrismNet
PrismNet, a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular conditions.
PrismNet
VRmol
VRmol provides the visualization and analysis of macromolecule structures in an infinite virtual environment on the web. VRmol is natively built with WebXR technology, providing functions in a fully immersive, inspiring virtual environment.
VRmol
Kitti Object Visualization
Data Transformation and Visualization for Kitti Object Detection Dataset, including mapping 3D boxes on LiDar point cloud in volumetric mode, mapping 2D and 3D boxes on Camera image, mapping 2D boxes on LiDar Birdview and mapping LiDar data on Camera image.
Kitti Object Visualization
3D DenseNet (torch)
Implementing a 3D DenseNet for 3D CAD Object Classification on ModelNet dataset using highly efficient Torch. VoxNet, C3D, and 3D-VGG models are also provided. Multi-GPU training is well supported. Check ModelNet Benchmark Leaderboard to see related researches.
3D DenseNet (torch)
3D Deep Learning
A collaborative list of 3D Deep Learning works via 3D Representation, 3D Classification, 3D Classification, 3D Object Detection, 3D Reconstruction & Generation, 3D Human Pose Estimation and their related datasets.
3D Deep Learning
Awesome CryoEM
A collaborative list of awesome CryoEM (Electron Cryo-Microscopy) resources, including Methods and Softwares related Particle Picking, 3D Classification, Denoising, Motion Correction, 3D Reconstruction, Model Building and some other related research.
Awesome CryoEM
V. Falconieri, S. Subramaniam, NCI-NIH

Academic Lectures

Deep learning based model building methods for Cryo-EM Density Map
Dec. 07 - Dec. 09 2020
Beijing, China
The 1st National Symposium on Cryo-electron Microscopy Software Development and Application
Oral presentation
Machine Intelligence Methods for Structural Biology on Protein, RNA and DNA
Sep. 24 - Sep. 29 2020
Beijing, China
The 13rd Biology Forum of Tsinghua Univeristy
Oral presentation
A2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes
Jun. 16 - Jun. 19 2019
Beijing, China
The 6th National Symposium on Cryo-electron Microscopy and Structural Biology
Poster Award
A2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes
Jan.27 - Feb.1 2019
Hawaii, USA
The 33rd AAAI Conference on Artificial Intelligence (AAAI 2019)
Spotlight presentation
Tools for macromolecular structure visualization and analysis: the next generation
Oct.27 - Oct. 29 2017
Beijing, China
The 10th Biology Forum of Tsinghua Univeristy
Oral presentation

Experience

Autonomous Driving Group, SenseTime Research, Beijing, China
Oct. 2017 – Dec. 2019
Computer Vision Research Intern
Advisor: Zhe Wang
Topic: 3D Object Detection, Molecular Structure Model Building
MMLab, the Chinese University of Hong Kong, Hong Kong, China
Jul. 2016 – Aug. 2016
Research Intern
Advisor: Hongsheng Li, Xiaogang Wang
Topic: 3D Density Volume Segmentaiton and Model Building
Multimedia Information Processing Lab, Peking University, Beijing, China
Jun. 2012 – Oct. 2012
Computer Vision Research Intern
Advisor: Yuxin Peng
Topic: TRECVID 2012, Known-Item Search, Image Text Retrieval and Natural Language Processing.