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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Ke Han, Xiaosong Li, Xiaoran Liang, Yanchun Xu, Dong Guan, Renchu Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Changchun China Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun China Zhuhai College of Science and Technology Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai China University of Missouri Christopher S. Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350386226

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy. © 2024 IEEE.

关键词： knowledge graph

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Applying Hybrid Clustering in Pulsar Candidate Sifting with Multi-modality for FAST Survey

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Research in Astronomy and Astrophysics 2024年第3期24卷 283-296页

作者： Zi-Yi You Yun-Rong Pan Zhi Ma Li Zhang Shuo Xiao Dan-Dan Zhang Shi-Jun Dang Ru-Shuang Zhao Pei Wang Ai-Jun Dong Jia-Tao Jiang Ji-Bing Leng Wei-An Li Si-Yao Li School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Key Laboratory of Information and Computing Guizhou Province Guizhou Normal UniversityGuiyang 550001China School of Big data and Computer Science Guizhou Normal UniversityGuiyang 550025China Guizhou Software Engineering Research Center Guiyang 550000China

Pulsar search is always the basis of pulsar navigation,gravitational wave detection and other research ***,the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope(FAST)shows an explosive growth rate that has brought challenges for its pulsar candidate filtering ***,the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification *** this study,a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented,which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for *** on both High Time Resolution Universe SurveyⅡ(HTRU2)and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars:On HTRU2,the precision and recall rates of its parallel mode reach0.981 and 0.988 *** FAST data,those of its parallel mode reach 0.891 and 0.961,meanwhile,the running time also significantly decreases with the increment of parallel nodes within ***,we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.

关键词： methods data analysis-surveys-methods numerical

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Security challenges and defense approaches for blockchain-based services from a full-stack architecture perspective

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Blockchain(Research and Applications) 2023年第3期4卷 18-38页

作者： Hongsong Chen Xietian Luo Lei Shi Yongrui Cao Yongpeng Zhang Department of Computer Science University of Science and Technology Beijing(USTB)Beijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China

As an advantageous technique and service,the blockchain has shown great development and application ***,its security has also met great challenges,and many security vulnerabilities and attack issues in blockchain-based services have ***,security issues of blockchain have attracted extensive ***,there is still a lack of blockchain security research from a full-stack architecture perspective,as well as representative quantitative experimental reproduction and *** aim to provide a security architecture to solve security risks in blockchain services from a full-stack architecture ***,we propose a formal definition of the full-stack security architecture for blockchain-based services,and we also propose a formal expression of security issues and defense solutions from a full-stack security *** use ConCert to conduct a smart contract formal verification experiment by property-based *** security vulnerabilities of blockchain services in the Common Vulnerabilities and Exposures(CVE)and China Nation Vulnerability database(CNVD)are selected and ***,three real contract-layer real attack events are reproduced by an experimental *** Alibaba's blockchain services and Identity Mixer in Hyperledger Fabric as a case study,the security problems and defense techniques are analyzed and *** last,the future research directions are proposed.

关键词： Security and privacy Blockchain-based services Full stack Architecture perspective Case and experimental study Formal verification

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Visual abstraction of dynamic network via improved multi-class blue noise sampling

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Frontiers of Computer Science 2023年第1期17卷 171-185页

作者： Yanni PENG Xiaoping FAN Rong CHEN Ziyao YU Shi LIU Yunpeng CHEN Ying ZHAO Fangfang ZHOU School of Computer Science and Engineering Central South UniversityChangsha 410083China Institute of Big Data Hunan University of Finance and EconomicsChangsha 410205China Rail Data Research and Application Key Laboratory of Hunan Province Changsha 410083China

Massive sequence view (MSV) is a classic timeline-based dynamic network visualization approach. However, it is vulnerable to visual clutter caused by overlapping edges, thereby leading to unexpected misunderstanding of time-varying trends of network communications. This study presents a new edge sampling algorithm called edge-based multi-class blue noise (E-MCBN) to reduce visual clutter in MSV. Our main idea is inspired by the multi-class blue noise (MCBN) sampling algorithm, commonly used in multi-class scatterplot decluttering. First, we take a node pair as an edge class, which can be regarded as an analogy to classes in multi-class scatterplots. Second, we propose two indicators, namely, class overlap and inter-class conflict degrees, to measure the overlapping degree and mutual exclusion, respectively, between edge classes. These indicators help construct the foundation of migrating the MCBN sampling from multi-class scatterplots to dynamic network samplings. Finally, we propose three strategies to accelerate MCBN sampling and a partitioning strategy to preserve local high-density edges in the MSV. The result shows that our approach can effectively reduce visual clutters and improve the readability of MSV. Moreover, our approach can also overcome the disadvantages of the MCBN sampling (i.e., long-running and failure to preserve local high-density communication areas in MSV). This study is the first that introduces MCBN sampling into a dynamic network sampling.

关键词： dynamic network visualization massive sequence view multi-class blue noise sampling visual abstraction

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Spatiotemporal deformation analysis of glaciers and surrounding landscapes in the Shishapangma region using InSAR techniques

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Journal of Mountain Science 2025年第1期22卷 210-229页

作者： LIANG Wenli LIU Rui JI Qin KOU Pinglang Chongqing Key Laboratory of GIS Application School of Geography and TourismChongqing Normal UniversityChongqing 401331China National Earth System Science Data Center Beijing 100000China Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism(TMDPDMCT)Chongqing University of Posts and TelecommunicationsChongqing 400065China

Glacier dynamics in the Himalayan midlatitudes,particularly in regions like the Shishapangma,are not yet fully understood,especially the localized topographic and climatic impacts on glacier *** study analyzes the spatiotemporal characteristics of glacier surface deformation in the Shishapangma region using the Small Baseline Subset(SBAS)Interferometric Synthetic Aperture Radar(In SAR)*** analysis reveals an average deformation rate of-4.02±17.65 mm/yr across the entire study area,with glacier regions exhibiting significantly higher rates of uplift(16.87±13.20 mm/yr)and subsidence(20.11±14.55 mm/yr)compared to non-glacier *** identifies significant surface lowering on the mountain flanks and localized uplift in certain catchments,emphasizing the higher deformation rates in glacial areas compared to non-glacial *** found a strong positive correlation between temperature and cumulative deformation(correlation coefficient of 0.63),particularly in glacier areas(0.82).The research highlights the role of temperature as the primary driver of glacier wastage,particularly at lower elevations,with strong correlations found between temperature and cumulative *** also indicates the complex interactions between topographic features,notably,slope gradient,which shows a positive correlation with subsidence rates,especially for slopes below 35°.South-,southwest-,and west-facing slopes exhibit significant uplift,while north-,northeast-,and east-facing slopes predominantly ***,we identified transition zones between debris-covered glaciers and clean ice as areas of most intense deformation,with average rates exceeding 30 mm/yr,highlighting these as potential high-risk zones for *** study comprehensively analyzes the deformation characteristics in both glacier and non-glacier areas in the Shishapangma region,revealing the complex interplay of topographic,climatic,and hydrological factors influencing glacier dynamic

关键词： Glacier melting SBAS-InSAR Topographic factors Temperature change Middle Himalayas

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FORECASTING SHORT-TERM PASSENGER FLOW ON A BUS ROUTE: A SPLITTING–INTEGRATING METHOD BASED ON PASSENGER TRAVEL BEHAVIOR

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Transport 2025年第1期40卷 12-23页

作者： Fang, Xiaoping Lin, Mei Chen, Weiya Pan, Xin School of Traffic and Transportation Engineering Rail Data Research and Application Key Laboratory of Hunan Province Central South University Changsha China Institute of Transportation Development Strategy and Planning of Sichuan Province China

Short-term passenger flow forecasting is the key to implement real-time dynamic dispatching of buses, which can meet the travel time requirement of passengers with different attributes. In practice, it is difficult to obtain passenger attribute information due to the restriction of bus information systems or other conditions. This article proposes a new perspective on identifying passenger attribute information, that is, the correlation between the bus card number and the travel time is used to analyse passenger travel behaviour. Then using the travel frequency as the splitting boundary, the passenger set is split into different types of subsets, which are predicted by different methods. The total forecast values are obtained by integration, so as to explore the effectiveness of the passenger attribute identification and splitting–integrating method. The result shows that: (1) compared with the forecasting method without considering the passenger travel behaviour, the performance of splitting–integrating method is better, and the passenger attribute identification method is effective;(2) the value of the splitting boundary will affect the size and consistency of the subset, and the optimal value can be sought according to forecast results;(3) different types of subsets should be treated by different forecasting models and combination paths. © 2025 The Author.

关键词： Travel time

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Self-supervised probabilistic models for exploring shape memory alloys

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npj Computational Materials 2024年第1期10卷 1338-1347页

作者： Yiding Wang Tianqing Li Hongxiang Zong Xiangdong Ding Songhua Xu Jun Sun Turab Lookman State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an710049China National Engineering Laboratory for Big Data Analytics School of Mathematics and StatisticsXi’an Jiaotong UniversityXi’an710049China AiMaterials Research LLC Santa FeNM87501USA

Recent advancements in machine learning(ML)have revolutionized the field of high-performance materials ***,developing robust ML models to decipher intricate structure-property relationships in materials remains challenging,primarily due to the limited availability of labeled datasets with well-characterized crystal *** is particularly pronounced in materials where functional properties are closely intertwined with their crystallographic *** a selfsupervised probabilistic model(SSPM)that autonomously learns unbiased atomic representations and the likelihood of compounds with given crystal structures,utilizing solely the existing crystal structure data from materials *** significantly enhances the performance of downstream ML models by efficient atomic representations and accurately captures the probabilistic relationships between composition and crystal *** showcase SSPM’s capability by discovering shapememory alloys(SMAs).Amongst the top 50 predictions,23 have been confirmed as SMAs either experimentally or theoretically,and a previously unknown SMA candidate,MgAu,has been identified.

关键词： alloys probabilistic shape

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CHDbase:A Comprehensive knowledgebase for Congenital Heart Disease-related Genes and Clinical Manifestations

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Genomics, Proteomics & Bioinformatics 2023年第1期21卷 216-227页

作者： Wei-Zhen Zhou Wenke Li Huayan Shen Ruby W.Wang Wen Chen Yujing Zhang Qingyi Zeng Hao Wang Meng Yuan Ziyi Zeng Jinhui Cui Chuan-Yun Li Fred Y.Ye Zhou Zhou State Key Laboratory of Cardiovascular Disease Beijing Key Laboratory for Molecular Diagnostics of Cardiovascular DiseasesCenter of Laboratory MedicineFuwai HospitalNational Center for Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100037China International Joint Informatics Laboratory&Jiangsu Key Laboratory of Data Engineering and Knowledge Service School of Information ManagementNanjing UniversityNanjing 210023China Institute of Molecular Medicine Peking UniversityBeijing 100871China

Congenital heart disease(CHD)is one of the most common causes of major birth defects,with a prevalence of 1%.Although an increasing number of studies have reported the etiology of CHD,the findings scattered throughout the literature are difficult to retrieve and utilize in research and clinical *** therefore developed CHDbase,an evidence-based knowledgebase of CHD-related genes and clinical manifestations manually curated from 1114 publications,linking 1124 susceptibility genes and 3591 variations to more than 300 CHD types and related *** such as the information of each publication and the selected population and samples,the strategy of studies,and the major findings of studies were integrated with each item of the research *** also integrated functional annotations through parsing50 databases/tools to facilitate the interpretation of these genes and variations in disease *** further prioritized the significance of these CHD-related genes with a gene interaction network approach and extracted a core CHD sub-network with 163 *** clear genetic landscape of CHD enables the phenotype classification based on the shared genetic ***,CHDbase provides a comprehensive and freely available resource to study CHD susceptibilities,supporting a wide range of users in the scientific and medical *** is accessible at http://***.

关键词： Congenital heart disease Congenital heart defect database Genetics Classification

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PDDD-PreTrain:A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

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Plant Phenomics 2023年第2期5卷 313-332页

作者： Xinyu Dong Qi Wang Qianding Huang Qinglong Ge Kejun Zhao Xingcai Wu Xue Wu Liang Lei Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China The School of Physics and Optoelectronic Engineering Guangdong University of TechnologyGuangzhou 510006China National Key Laboratory of Green Pesticide Key Laboratory of Green Pesticide and Agricultural BioengineeringMinistry of EducationGuizhou UniversityGuiyang 550025China

Plant diseases threaten global food security by reducing crop yield;thus,diagnosing plant diseases is critical to agricultural *** intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming,costly,inefficient,and subjective *** a mainstream AI method,deep learning has substantially improved plant disease detection and diagnosis for precision *** the meantime,most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased ***,the commonly used pre-trained models are from the computer vision dataset,not the botany dataset,which barely provides the pre-trained models sufficient domain knowledge about plant ***,this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic *** address this issue,we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease *** addition,we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification,plant disease detection,plant disease segmentation,and other *** extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time,thereby supporting the better diagnosis of plant *** addition,our pre-trained models will be open-sourced at https://***/and Zenodo platform https://***/10.5281/zenodo.7856293.

关键词： diagnosis Plant thereby

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AMHF-TP:Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features

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Quantitative Biology 2025年第1期13卷 127-141页

作者： Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’anChina Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’anChina Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anChina School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’anChina

Multifunctional therapeutic peptides(MFTP)hold immense potential in diverse therapeutic contexts,yet their prediction and identification remain challenging due to the limitations of traditional methodologies,such as extensive training durations,limited sample sizes,and inadequate generalization *** address these issues,we present AMHF-TP,an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance *** AMHF-TP is composed of four key components:a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences;a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures;a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences;and a hierarchical feature extraction module that integrates multimodal peptide sequence *** with leading methods,the proposed AMHF-TP demonstrates superior precision,accuracy,and coverage,underscoring its effectiveness and robustness in MFTP *** comparative analysis of separate hierarchical models and the combined model,as well as with five contemporary models,reveals AMHFTP’s exceptional performance and stability in recognition tasks.

关键词： deep learning hypergraph multifunctional therapeutic peptides multi-granularity hierarchical features

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