Zhen He

Zhen He
The Beijing Institute of Basic Medical Sciences

Doctor of Philosophy

About

15
Publications
6,960
Reads
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166
Citations
Additional affiliations
January 2019 - present
Beijing Institute of Basic Medical Sciences
Position
  • Professor (Associate)
Description
  • Computational Biology, Machine Learning
January 2016 - February 2018
University College London
Position
  • Research Assistant
Description
  • Machine Learning
Education
March 2014 - December 2018
National University of Defense Technology
Field of study
  • Autonomous Driving, Machine Learning

Publications

Publications (15)
Article
Full-text available
Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-leve...
Article
Full-text available
Sequence logos are used to visually display conservations and variations in short sequences. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators are based on the assumption that all the input sequences are from the same homologous group, which will le...
Preprint
Full-text available
Sequence logos are used to visually display sequence conservations and variations. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators can only draw logos for sequences of the same length, let alone for groups of sequences with different characteristi...
Article
Full-text available
Liver cirrhosis (LC) has been associated with gut microbes. However, the strain diversity of species and its association with LC have received little attention. Here, we constructed a computational framework to study the strain heterogeneity in the gut microbiome of patients with LC. Only Faecalibacterium prausnitzii shows different single-nucleoti...
Article
Objective: To explore the applicability of recurrent neural networks to rapid detection of coronavirus nucleotide sequences in high-throughput sequencing data, and offer new ideas about rapid and accurate identification of new and highly variant coronavirus sequences. Methods: Coronavirus and human genome data was obtained from NCBI, and the collec...
Conference Paper
Full-text available
Online Multi-Object Tracking (MOT) from videos is a challenging computer vision task which has been extensively studied for decades. Most of the existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm combined with popular machine learning approaches which largely reduce the human effort to tune algorithm parameters. However,...
Preprint
Online Multi-Object Tracking (MOT) from videos is a challenging computer vision task which has been extensively studied for decades. Most of the existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm combined with popular machine learning approaches which largely reduce the human effort to tune algorithm parameters. However ,...
Preprint
Online Multi-Object Tracking (MOT) from videos is a challenging computer vision task which has been extensively studied for decades. Most of the existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm combined with popular machine learning approaches which largely reduce the human effort to tune algorithm parameters. However,...
Article
Full-text available
Nowadays, video surveillance has become ubiquitous with the quick development of artificial intelligence. Multi-object detection (MOD) is a key step in video surveillance and has been widely studied for a long time. The majority of existing MOD algorithms follow the “divide and conquer” pipeline and utilize popular machine learning techniques to op...
Article
Full-text available
Modelling the multimedia data such as text, images, or videos usually involves the analysis, prediction, or reconstruction of them. The recurrent neural network (RNN) is a powerful machine learning approach to modelling these data in a recursive way. As a variant, the long short-term memory (LSTM) extends the RNN with the ability to remember inform...
Conference Paper
Full-text available
Long Short-Term Memory (LSTM) is a popular approach to boosting the ability of Recurrent Neural Networks to store longer term temporal information. The capacity of an LSTM network can be increased by widening and adding layers. However, the former introduces additional parameters, while the latter increases the runtime. As an alternative we propose...
Article
Full-text available
Long Short-Term Memory (LSTM) is a popular approach to boosting the ability of Recurrent Neural Networks to store longer term temporal information. The capacity of an LSTM network can be increased by widening and adding layers. However, usually the former introduces additional parameters, while the latter increases the runtime. As an alternative we...
Conference Paper
Full-text available
This paper presents a practical trajectory planning framework towards fully autonomous driving in urban environments. Firstly, based on the behavioral decision commands , a reference path is extracted from the digital map using the LIDAR-based localization information. The reference path is refined and interpolated via a nonlinear optimization algo...
Conference Paper
Full-text available
GPS is very popular for locating the unmanned vehicles. However, it cannot solve all the problems about location. As crossroad is very important for unmanned vehicles, it is necessary for unmanned vehicles to recognize the crossroad and to load some other recognition tasks (such as traffic light recognition and traffic sign recognition) at the same...
Conference Paper
Full-text available
Many road detection algorithms require pre-learned information, which may be unreliable as the road scene is usually unexpectable. Single image based (i.e., without any pre-learned information) road detection techniques can be adopted to overcome this problem, while their robustness needs improving. To achieve robust road detection from a single im...

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