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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 672-683页

作者： Yiran Huang Qiulong Pu Wei Lan Cuiyu Huang Cheng Zhong School of Computer Electronics and Information Guangxi University Nanning China Guangxi Universities Key Laboratory of Parallel and Distributed Computing and Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China College of Chemistry Nankai University Tianjin China

Identifying disease-associated metabolites could provide critical clues for the diagnosis and treatment of diseases. Although computational approaches have been proposed to predict disease-associated metabolites by training models using positive and negative samples, few efforts have paid attention to optimize the reliability of negative samples, which could possibly improve the prediction accuracy of model. In this work, we propose a novel method called SMDPG to leverage optimized negative sampling and sparse graph convolutional network to predict metabolite-disease associations. In SMDPG, we first build a metabolite-disease bipartite graph based on less similar metabolites and diseases and propose a negative sampling method to select reliable negative metabolite-disease samples from the bipartite graph to reduce the effect of noisy samples. Then a homogeneous metabolite-disease pair graph is constructed based on the selected negative samples and known metabolite-disease associations. Further, an edge sparseness operation is designed to simplify the connection of the homogeneous graph. Finally, the simplified homogeneous graph is fed into the graph convolutional network to predict metabolite-disease associations. Experimental results show that SMDPG can more accurately predicts metabolite-disease associations as compared to the existing methods. Moreover, the study case demonstrates that SMDPG is an effective framework for identifying potential metabolite-disease associations.

关键词： Diseases Feature extraction Semantics Databases Reliability Graph convolutional networks Training Sparse matrices Predictive models Bipartite graph

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An End-to-End Classification Network Model Based on Hybrid Supervision for Industrial Surface Defect Detection

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International Journal of Pattern Recognition and Artificial Intelligence 2025年第5期39卷

作者： Qin, Runbing Chen, Ningjiang Gan, Shukun Guangxi University School of Computer Electronics and Information Nanning530004 China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Nanning530004 China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning530004 China

Industrial part surface defect detection aims to precisely locate defects in images, which is crucial for quality control in manufacturing. The traditional method needs to be designed in advance, but it has shortcomings in terms of generalization ability. Deep neural network-based defect detection faces issues such as low resolution, data scarcity, labeling costs, and high computation. Therefore, an improved solution is necessary to enhance industrial product quality control efficiency and accuracy. A new method is proposed that utilizes end-to-end training of a two-stage neural network based on segmentation with an extended training process. The gradient flow from the classification to the segmentation network is adjusted to prevent unstable features from interfering with learning. To address the problem of image oversampling and undersampling during training, a frequency sampling scheme for negative samples is introduced. Additionally, positive pixels in the region-based segmentation mask are weighted using the distance transform algorithm so that regions with a high probability of defects can be detected without detailed annotation. Experiments are conducted across three distinct defect datasets by applying hybrid supervision encompassing diverse conditions. In the optimal case, the AP rate of the proposed model on the DAGM and KolektorSDD datasets reaches 100%, and the AP rate on the Severstal Steel dataset reaches 98.99%. The experiments’ results show that the detection accuracy has improved greatly, showing how well the proposed method works in the industry. © 2025 World Scientific Publishing Company.

关键词： Deep neural networks

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parallel Accelerating Ultra-Long Read Alignment by Vertical Partitioning Data 13

Parallel Accelerating Ultra-Long Read Alignment by Vertical ...

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13th IEEE International Symposium on parallel Architectures, Algorithms and Programming, PAAP 2022

作者： Pan, Deng Zhong, Cheng Chen, Danyang Zhang, Jinxiong Yang, Feng Guangxi University Key Laboratory of Parallel Distributed Computing Technology Guangxi Universities School of Computer Electronics and Information Guangxi Nanning China Guangxi University Key Laboratory of Parallel Distributed Computing School of Computer Electronics and Information Guangxi Nanning China

ISBN: (纸本)9781665452182

The alignment between sequencing reads and genome is a basic work in biological big data analysis. Each read of the third generation sequencing data is getting longer, and the data size is getting larger. To effectively solve the ultra-long read alignment problem with high requirements for computing and memory capacity, a strategy for vertical partitioning ultra-long reads on hybrid CPU/GPU cluster is proposed, and a heap data structure is used to filter the local aligned results in all computing nodes of the parallel cluster system according to the alignment score to reduce the data transmission size. The methods for early termination and parallel merging-splicing are used to accelerate splicing local aligned results. The local aligned results among all computing nodes are collected and extended to obtain the final alignment results. The experimental results on datasets of simulated and real ultra-long reads show that the proposed parallel alignment algorithm can obtain high alignment accuracy, sensitivity and base-level sensitivity as a whole, and accelerate completing alignment between ultra-long reads and reference genome. © 2022 IEEE.

关键词： Alignment

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PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning

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Ad Hoc Networks 2025年 176卷

作者： Ou, Pingjie Chen, Ningjiang Yang, Long School of Computer and Electronic Information Guangxi University Nanning Guangxi530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Nanning Guangxi530004 China The Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning Guangxi530004 China

In vehicular networks, onboard devices face the challenge of limited storage, and computational resources constrain their processing and storage capabilities. This limitation is particularly significant for applications that require complex computations and real-time responses. Additionally, limited storage capacity reduces the range of cacheable data, which can impact the immediate availability of data and the continuity of services. Therefore, improving cache utilization and meeting vehicles’ real-time data demands pose significant challenges. Deep reinforcement learning can optimize the issues arising from agents’ continuously changing state and action spaces due to increasing request demands. However, training the network may encounter instability and convergence difficulties in dynamic and complex environments or situations with sparse rewards. In response to these issues, this paper proposes a Priority-based Deep Reinforcement Learning Collaborative Cache Management method (PDRL-CM). PDRL-CM first designs a lightweight cache admission strategy that leverages data's inherent and combined attributes. It then makes cache admission decisions Using Monte Carlo sampling and a max-value search strategy combined with a feedforward neural network. Secondly, the method considers minimizing system latency and reducing vehicle energy consumption as joint optimization problems. An improved deep reinforcement learning algorithm solves this problem and makes cache-sharding decisions. A prioritized experience replay mechanism is incorporated to adjust the network prediction model quickly and accelerate the convergence process. Experimental results indicate that, compared to existing DRL-based caching methods, PDRL-CM offers faster data processing efficiency and higher cache hit rates under varying vehicle density, storage capacity, and content volume conditions. © 2025

关键词： Cache memory

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Enhancing Resolution of Inferring Hi-C Data Integrating U-Net and ResNet Networks 23rd

Enhancing Resolution of Inferring Hi-C Data Integrating U-Ne...

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23rd International Conference on parallel and distributed computing, Applications, and Technologies, PDCAT 2022

作者： Zhao, Fengjiao Li, Na Zhong, Cheng School of Computer Electronics and Information Guangxi University Guangxi Nanning530004 China Key Laboratory of Parallel and Distributed Computing Technology in Guangxi Universities Guangxi Nanning530004 China

ISBN: (纸本)9783031299261

The Hi-C data is the basis of three-dimensional chromosome structure reconstruction. In this paper, a method integrating U-Net and ResNET network is proposed to infer high resolution Hi-C data from existing low-resolution Hi-C data. Firstly, the U-Net is used as the backbone network to make full use of the shallow layer details and deep layer essential features of Hi-C data. Secondly, the residual module is introduced into the coding path and decoding path of the network to increase the depth of the model and prevent network degradation. Finally, the down sampling is used to reduce the calculation amount to decrease the required running time for the network model. The experimental results on real datasets showed that the proposed method had better performance than existing methods, and the inferred high resolution Hi-C data was closer to the real Hi-C data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chromosomes

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CCN hotspot cache placement strategy based on genetic algorithm 10th

CCN hotspot cache placement strategy based on genetic algori...

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10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017

作者： Wu, Hongjia Wang, Nao Wang, Gaocai School of Computer and Electronic Information Guangxi University NanningGuangxi533004 China Guangxi Colleges and University Key Laboratory of Parallel and Distributed Computing Technology Nanning530004 China

ISBN: (纸本)9783319723945

Content-centric networks provide effective support for content services through a caching mechanism, so the caching of the placement strategy has become a hot research. In this paper, the problem of content placement in CCN is taken as the starting point, focusing on the allocation of caching resources for district partitioning according to the number of users given the content of predictable hotspots. When the hot topic breaks out, the first to the user from the content of the nearest as the goal, from the user’s point of view as its characteristics, nested using improved genetic algorithms to select hotspot cache placement points, and a CCN hotspot cache placement strategy based on genetic algorithm is proposed. In the simulation experiment, the cache placement strategy proposed in this paper is compared with other traditional strategies in network performance. The simulation results show that the strategy can effectively reduce the access hops and average access latency, reduce the server load and improve the user satisfaction. © Springer International Publishing AG 2017.

关键词： Genetic algorithms

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An improved pre-copy transmission algorithm in mobile cloud computing 1

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10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017

作者： Huang, Xianfei Wang, Nao Wang, Gaocai School of Computer and Electronic Information Guangxi University NanningGuangxi530004 China Guangxi Colleges and University Key Laboratory of Parallel and Distributed Computing Technology Nanning530004 China

ISBN: (数字)9783319723891

ISBN: (纸本)9783319723884

With the rapidly development of mobile cloud computing, in order to provide a better experience for mobile users, the computing and storage functions of mobile terminals are migrated to the cloud. In the application research of virtual machine migration, the transmission rate of wireless channel will changed with the changes of network condition. This paper focuses on the pre-copy transmission algorithm in the process of virtual machine migration in mobile cloud computing. A pre-copy transmission algorithm based on optimal stopping theory (PTAOST) was proposed, the algorithm constructs an optimal stopping problem based on an optimal transmission rate in the process of virtual machine migration. And we solve the model by using the optimal stopping theory, and get an optimal transfer rate. So the total amount of data and migration time is reduced during the process of virtual machine migration. In simulations, the PTAOST is compared with the transmission algorithms offered by some literatures, and we obtained some comparable results about the total migration data and total migration time to different transmission algorithms. The simulative results show that the proposed algorithm has less the total amount of migration data and total time of migration and improves the performance of migration. © 2017, Springer International Publishing AG.

关键词： Mobile cloud computing

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Direct method-green's theory: From PDE to BIE in the geometric transformation

Direct method-green's theory: From PDE to BIE in the geometr...

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2016 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2016

作者： Yang, Li-Na Li, Tao-Shen Tang, Yuan Yan Xu, Jia Pan, Jian-Jia Luo, Hui-Wu Zheng, Xian-Wei School of Computer Electronics and Information Guangxi University Nanning530004 China Department of Computer and Information Science Faculty of Science and Technology University of Macau China Guangxi Colleges Universities Key Laboratory of Parallel and Distributed Computing Nanning530004 China

ISBN: (纸本)9781509035885

In this research, we apply the Green's theory for converting the partial differential equation to the boundary integral equation for geometric transformation. Green's theory is designed specifically for integral equation. It is efficient in detecting the singularity point to the geometric transformation that has been verified. Experimental results show that the Green's theory has good performance. © 2016 IEEE.

关键词： Partial differential equations

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parallel Accelerating Ultra-Long Read Alignment by Vertical Partitioning Data

Parallel Accelerating Ultra-Long Read Alignment by Vertical ...

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International Symposium on parallel Architectures, Algorithms and Programming (PAAP)

作者： Deng Pan Cheng Zhong Danyang Chen Jinxiong Zhang Feng Yang School of Computer Electronics and Information Key Laboratory of Parallel Distributed Computing Technology in Guangxi Universities Guangxi University Nanning Guangxi China School of Computer Electronics and Information Key Laboratory of Parallel Distributed Computing Guangxi University Nanning Guangxi China

ISBN: (纸本)9781665452199

关键词： Sequential analysis Sensitivity Splicing Merging Memory management Genomics Programming

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Efficient distributed parallel Aligning Reads and Reference Genome with Many Repetitive Subsequences Using Compact de Bruijn Graph

Efficient Distributed Parallel Aligning Reads and Reference ...

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International Symposium on parallel Architectures, Algorithms and Programming (PAAP)

作者： Yao Li Cheng Zhong Danyang Chen Jinxiong Zhang Mengxiao Yin Key Laboratory of Parallel School of Computer Electronics and Information Distributed Computing Technology in Guangxi Universities Guangxi University Nanning China

ISBN: (纸本)9781665496407

A large number of reads generated by the next generation sequencing platform will contain many repetitive subsequences. Effective localizing and identifying genomic regions containing repetitive subsequences will contribute to the subsequent genomic data analysis. To accelerate the alignment between large-scale short reads and reference genome with many repetitive subsequences, this paper develops a compact de Bruijn graph based short-read alignment algorithm on distributed parallel computing platform. The algorithm uses resilient distributed data sets (RDDS) to perform calculations in memory, and executes the broadcast method to distribute short reads and reference genome to the computing nodes to reduce the data communication time on the cluster system, and the number of RDD partitions is set to optimize the performance of parallel aligning algorithm. Experimental results on real datasets show that compared with the compact de Bruijn graph based sequential short-read alignment algorithm, our implemented distributed parallel alignment algorithm achieves good acceleration on the premise of obtaining the same correct alignment percentage as a whole, and compared with existing distributed parallel alignment algorithms, the implemented parallel algorithm can more quickly complete the alignment between large-scale short reads and reference genome with highly repetitive subsequences.

关键词： Sequential analysis Genomics Clustering algorithms distributed databases Programming Partitioning algorithms parallel architectures

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