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Chinese Journal of Electronics 2021年第5期30卷 815-823页

作者： WANG Chao zou quan Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Hainan Key Laboratory for Computational Science and Application Hainan Normal University

Several Coronaviruses(CoVs) are epidemic pathogens that cause severe respiratory syndrome and are associated with significant morbidity and mortality. In this paper, a machine learning method was developed for predicting the risk of human infection posed by CoVs as an early warning system. The proposed Spike-SVM(Support vector machine) model achieved an accuracy of 97.36% for Human-infective CoV(HCoV) and Nonhuman-infective CoV(Non-HCoV) classification. The top informative features that discriminate HCoVs and Non-HCoVs were identified. Spike-SVM is anticipated to be a useful bioinformatics tool for predicting the infection risk posed by CoVs to humans.

关键词： Spike protein NonHCoVs genetics microorganisms Support vector machine mortality Spike-SVM model Human-infective CoV severe respiratory syndrome machine learning method biology computing predicting Human-infective coronavirus diseases proteins HCoV human infection differentiating Nonhuman-infective CoV infection risk CoVs discriminate HCoVs learning (artificial intelligence) bioinformatics support vector machines informative features significant morbidity molecular biophysics early warning system epidemic pathogens Coronaviruses

Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level

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Research 2023年第4期2023卷 145-162页

作者： Ren Qi quan zou Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 324000China School of Life Science and Technology University of Electronic Science and Technology of ChinaChengduChina Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengduChina.

Cancer treatments always face challenging problems,particularly drug resistance due to tumor cell *** existing datasets include the relationship between gene expression and drug sensitivities;however,the majority are based on tissue-level *** drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative ***,machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene *** modeling using single-cell data and drug response information will not only improve machine learning for cell-drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer *** this paper,we review machine learning and deep learning approaches in drug *** analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis,we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell *** anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.

关键词： drugs expression overcome

Model-driven full system dynamics estimation of PMSM-driven chain shell magazine

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Defence Technology（防务技术） 2024年第9期39卷 147-156页

作者： Kai Wei Longmiao Chen quan zou School of Mechanical Engineering NanJing University of Science and TechnologyNanjing210094China

Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.

关键词： Chain shell magazine Full system dynamics estimation Disturbance estimation Parameter estimation Adaptive extended state observer

DeepM6ASeq-EL:prediction of human N6-methyladenosine(m^(6)A)sites with LSTM and ensemble learning

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Frontiers of Computer Science 2022年第2期16卷 27-33页

作者： Juntao CHEN quan zou Jing LI Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610051China Hainan Key Laboratory for Computational Science and Application Hainan Normal UniversityHaikou 571158China College of Intelligence and Computing Tianjin UniversityTianjin 300350China

N6-methyladenosine(m^(6)A)is a prevalent methylation modification and plays a vital role in various biological processes,such as metabolism,mRNA processing,synthesis,and *** studies have suggested that m^(6)A modification is related to common diseases such as cancer,tumours,and ***,accurate prediction of methylation sites in RNA sequences has emerged as a critical issue in the area of ***,traditional high-throughput sequencing and wet bench experimental techniques have the disadvantages of high costs,significant time requirements and inaccurate identification of *** through the use of traditional experimental methods,researchers have produced many large databases of m^(6)A *** the support of these basic databases and existing deep learning methods,we developed an m^(6)A site predictor named DeepM6ASeq-EL,which integrates an ensemble of five LSTM and CNN classifiers with the combined strategy of hard *** to the state-of-the-art prediction method WHISTLE(average AUC 0.948 and 0.880),the DeepM6ASeq-EL had a lower accuracy in m^(6)A site prediction(average AUC:0.861 for the full transcript models and 0.809 for the mature messenger RNA models)when tested on six independent datasets.

关键词： N6-methyladenosine site prediction LSTM CNN ensemble learning

String kernels construction and fusion:a survey with bioinformatics application

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Frontiers of Computer Science 2022年第6期16卷 145-158页

作者： Ren QI Fei GUO quan zou College of Intelligence and Computing Tianjin UniversityTianjin300350China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu610056China Hainan Key Laboratory for Computational Science and Application Hainan Normal UniversityHaikou571158China

The kernel method,especially the kernel-fusion method,is widely used in social networks,computer vision,bioinformatics,and other *** deals effectively with nonlinear classification problems,which can map linearly inseparable biological sequence data from low to high-dimensional space for more accurate differentiation,enabling the use of kernel methods to predict the structure and function of ***,the kernel method is significant in the solution of bioinformatics *** kernels applied in bioinformatics are explained clearly,which can help readers to select proper kernels to distinguish *** biological sequence data occur in practical *** of the use of machine learning methods to obtain knowledge,and how to explore the structure and function of biological methods for theoretical prediction,have always been emphasized in *** kernel method has gradually become an important learning algorithm that is widely used in gene expression and biological sequence *** review focuses on the requirements of classification tasks of biological sequence *** studies kernel methods and optimization algorithms,including methods of constructing kernel matrices based on the characteristics of biological sequences and kernel fusion methods existing in a multiple kernel learning framework.

关键词： multiple kernel learning kernel fusion methods support vector machines biological sequences analysis

Effect of Eliminating Edges on Robustness of Scale-Free Networks under Intentional Attack

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Chinese Physics Letters 2010年第6期27卷 270-272页

作者： LI Yong WU Jun zou An-quan Department of Business Administration Changsha University Changsha 410073 College of Information Systems and Management National University of Defense Technology Changsha 410073

We study the robustness of complex networks under edge elimination. We propose three different edge elimination strategies and investigate their effects on the robustness of scale-free networks under intentional attack. We show that deleting a proper fraction of edges connecting hub nodes and hub nodes can enhance the robustness of scale-free networks under intentional attack.

关键词： GRAPH theory ROBUST control PATHS & cycles (Graph theory) COMBINATORIAL analysis MATHEMATICAL physics

SBSM-Pro: support bio-sequence machine for proteins

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Science China(Information Sciences) 2024年第11期67卷 148-163页

作者： Yizheng WANG Yixiao ZHAI Yijie DING quan zou Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China

Proteins play a pivotal role in biological systems. The use of machine learning algorithms for protein classification can assist and even guide biological experiments, offering crucial insights for biotechnological applications. We introduce the support bio-sequence machine for proteins(SBSM-Pro), a model purpose-built for the classification of biological sequences. This model starts with raw sequences and groups amino acids based on their physicochemical properties. It incorporates sequence alignment to measure the similarities between proteins and uses a novel multiple kernel learning(MKL) approach to integrate various types of information, utilizing support vector machines for classification prediction. The results indicate that our model demonstrates commendable performance across ten datasets in terms of the identification of protein function and post translational modification. This research not only exemplifies state-of-the-art work in protein classification but also paves avenues for new directions in this domain, representing a beneficial endeavor in the development of platforms tailored for the classification of biological sequences. SBSM-Pro is available for access at http://***/soft/SBSM-Pro/.

关键词： protein classification machine learning multiple kernel learning sequence alignment

Identification of cytokine via an improved genetic algorithm

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Frontiers of Computer Science 2015年第4期9卷 643-651页

作者： Xiangxiang ZENG Sisi YUAN Xianxian HUANG quan zou Department of Computer Science Xiamen University Xiamen 361005 China

With the explosive growth in the number of pro- tein sequences generated in the postgenomic age, research into identifying cytokines from proteirls and detecting their biochemical mechanisms becomes increasingly important. Unfortunately, the identification of cytokines from proteins is challenging due to a lack of understanding of the struc- ture space provided by the proteins and the fact that only a small number of cytokines exists in massive proteins. In view of fact that a proteins sequence is conceptually similar to a mapping of words to meaning, n-gram, a type of probabilistic language model, is explored to extract features for proteins. The second challenge focused on in this work is genetic algo- rithms, a search heuristic that mimics the process of natural selection, that is utilized to develop a classifier for overcom- ing the protein imbalance problem to generate precise pre- diction of cytokines in proteins. Experiments carded on im- balanced proteins data set show that our methods outperform traditional algorithms in terms of the prediction ability.

关键词： n-grams genetic algorithm cytokine identifica-tion sampling imbalanced data

Synthesis, UV-curing Behavior and Surface Properties of New Fluorine-containing Aromatic Oxetane Monomers

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Chinese Journal of Polymer Science 2018年第4期36卷 521-527页

作者： Shu-Qing Fang Yu-Lian Pang Ying-quan zou College of Chemistry Beijing Normal University

In this paper, we combined high-end cationic UV-curable material with fluorinated chain obtaining a series of new fluorine-containing aromatic oxetane monomers via a mild nucleophilic substitution reaction. The structures and properties of monomers were characterized using ~1 H-NMR, ^（19） F-NMR, dynamic viscosity tests and differential scanning calorimetry（DSC）. It was determined that all of the fluorinated monomers obtained had much lower viscosity and higher thermostability after the introduction of hexafluorobenzene. Then, UV-curable coatings were prepared using four fluorine-containing aromatic oxetane monomers（FOX1-4）; the UV-curing kinetics, with three kinds of initiators, and properties of the cured films were evaluated using real-time Fourier transform infrared（FTIR） spectroscopy, water and diiodomethane contact angle tests, surface energy calculations and scanning electron microscopy（SEM）. The FTIR spectroscopy results showed that the coatings possessed excellent conversion rate（〉 99% with liquid initiator PAG-201 in 150 s）, and as the fluorine content increased, the monomers exhibited decreased mobility with the increasing viscosity and worse solubility with fluorinated monomers, resulting in a lower conversion rate. Moreover, the coatings possessed favorable hydrophobic and oleophobic properties and low surface energies owing to the fluoride chains floating to the membrane-air interface, which was also confirmed by discrete concave structures in SEM images. These new kinds of monomers can replace traditional fluorinated cationic monomers applied to the fingerprint resistant, fouling resistant, scratch resistant and anti-aging coatings, adhesives or printing ink materials.

关键词： Fluorine-containing monomers Oxetane UV-curing kinetics Surface properties

Biological Sequence Classification:A Review on Data and General Methods

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Research 2023年第3期2022卷 243-261页

作者： Chunyan Ao Shihu Jiao Yansu Wang Liang Yu quan zou School of Computer Science and Technology Xidian UniversityXi’anChina Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhouChina Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengduChina

With the rapid development of biotechnology,the number of biological sequences has grown *** continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence *** are many branches of biological sequence classification *** this review,we mainly focus on the function and modification classification of biological sequences based on machine ***-based prediction and analysis are the basic tasks to understand the biological functions of DNA,RNA,proteins,and ***,there are hundreds of classification models developed for biological sequences,and the quite varied specific methods seem dizzying at first ***,we aim to establish a long-term support website(http://***/~acy/BioseqData/***),which provides readers with detailed information on the classification method and download links to relevant *** briefly introduce the steps to build an effective model framework for biological sequence *** addition,a brief introduction to single-cell sequencing data analysis methods and applications in biology is also ***,we discuss the current challenges and future perspectives of biological sequence classification research.

关键词： steps exponentially hundreds