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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG college of Information science and Engineering Hunan Normal University School of computer science Shaanxi Normal University Faculty of computer science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences computer science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drugs Training Proteins Measurement Message passing Prediction methods Graph neural networks

The Squeeze & Excitation normalization Based nnU-Net for Segmenting Head & Neck Tumors

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Chinese Journal of Electronics 2024年第3期33卷 766-775页

作者： Juanying XIE Ying PENG Mingzhao WANG School of computer science Shaanxi Normal University

Head and neck cancer is one of the most common malignancies in the *** propose SE-nnU-Net by adapting SE(squeeze and excitation) normalization into nnU-Net,so as to segment head and neck tumors in PET/CT images by combining advantages of SE capturing features of interest regions and nnU-Net configuring itself for a specific *** basic module referred to convolution-ReLU-SE is designed for *** the encoder it is combined with residual structure while in the decoder without residual *** loss function combines Dice loss and Focal *** specific data preprocessing and augmentation techniques are developed,and specific network architecture is ***,the deep supervised mechanism is introduced to calculate the loss function using the last four layers of the decoder of *** SE-nnU-net is applied to HECKTOR 2020 and HECKTOR 2021 challenges,respectively,using different experimental *** experimental results show that SE-nnU-Net for HECKTOR 2020 obtained 0.745,0.821,and 0.725 in terms of Dice,Precision,and Recall,respectively,while the SE-nnU-Net for HECKTOR 2021 obtains 0.778 and 3.088 in terms of Dice and median HD95,*** SE-nnU-Net for segmenting head and neck tumors can provide auxiliary opinions for doctors diagnoses.

关键词： Image segmentation Head Data preprocessing Artificial neural networks Network architecture Data models Neck

YOLO-Drone:A Scale-Aware Detector for Drone Vision

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Chinese Journal of Electronics 2024年第4期33卷 1034-1045页

作者： Yutong LI Miao MA Shichang LIU Chao YAO Longjiang GUO School of computer science Shaanxi Normal University Key Laboratory of Modern Teaching Technology Ministry of Education college of computer science Sichuan University

Object detection is an important task in drone vision. Since the number of objects and their scales always vary greatly in the drone-captured video, small object-oriented feature becomes the bottleneck of model performance, and most existing object detectors tend to underperform in drone-vision scenes. To solve these problems, we propose a novel detector named YOLO-Drone. In the proposed detector, the backbone of YOLO is firstly replaced with ConvNeXt, which is the state-of-the-art one to extract more discriminative features. Then, a novel scale-aware attention(SAA) module is designed in detection head to solve the large disparity scale problem. A scale-sensitive loss(SSL) is also introduced to put more emphasis on object scale to enhance the discriminative ability of the proposed detector. Experimental results on the latest VisDrone 2022 test-challenge dataset(detection track) show that our detector can achieve average precision(AP) of 39.43%, which is tied with the previous state-of-the-art, meanwhile,reducing 39.8% of the computational cost.

关键词： YOLO Costs Object oriented modeling Computational modeling Detectors Feature extraction Data models

An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of computer science Shaanxi Normal University Faculty of computer science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Department of computer science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Proteins RNA Neural networks Encoding Robustness Decoding Convolutional neural networks

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A privacy-preserving vehicle trajectory clustering framework

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Frontiers of Information Technology & Electronic Engineering 2024年第7期25卷 988-1002页

作者： Ran TIAN Pulun GAO Yanxing LIU college of computer science&Engineering Northwest Normal UniversityLanzhou 730070China

As one of the essential tools for spatio‒temporal traffic data mining,vehicle trajectory clustering is widely used to mine the behavior patterns of ***,uploading original vehicle trajectory data to the server and clustering carry the risk of privacy ***,one of the current challenges is determining how to perform vehicle trajectory clustering while protecting user *** propose a privacy-preserving vehicle trajectory clustering framework and construct a vehicle trajectory clustering model(IKV)based on the variational autoencoder(VAE)and an improved K-means *** the framework,the client calculates the hidden variables of the vehicle trajectory and uploads the variables to the server;the server uses the hidden variables for clustering analysis and delivers the analysis results to the *** IKV’workflow is as follows:first,we train the VAE with historical vehicle trajectory data(when VAE’s decoder can approximate the original data,the encoder is deployed to the edge computing device);second,the edge device transmits the hidden variables to the server;finally,clustering is performed using improved K-means,which prevents the leakage of the vehicle *** is compared to numerous clustering methods on three *** the nine performance comparison experiments,IKV achieves optimal or sub-optimal performance in six of the ***,in the nine sensitivity analysis experiments,IKV not only demonstrates significant stability in seven experiments but also shows good robustness to hyperparameter *** results validate that the framework proposed in this paper is not only suitable for privacy-conscious production environments,such as carpooling tasks,but also adapts to clustering tasks of different magnitudes due to the low sensitivity to the number of cluster centers.

关键词： Privacy protection Variational autoencoder Improved K-means Vehicle trajectory clustering

The Optimization of Binary Randomized Response Based on Lanke Privacy and Utility Analysis

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Chinese Journal of Electronics 2024年

作者： Yihui ZHOU Wenli WANG Jun YAN Zhenqiang WU Laifeng LU School of computer science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University School of Mathematics and computer Applications Shangluo College

Currently, it has become a consensus to enhance privacy protection. Randomized response(RR) technique, as the mainstream perturbation mechanism for local differential privacy, has been widely studied. However,most of the research in literature managed to modify existing RR schemes and propose new mechanisms with better privacy protection and utility, which are illustrated only by numerical experiments. We study the properties of generalized binary randomized response mechanisms from the perspectives of Lanke privacy and utility. The mathematical expressions of privacy and utility for the binary RR mechanism are given respectively. Moreover, the comparison principle for privacy and utility of any two mechanisms is proved. Finally, the optimization problem of the binary RR mechanism is discussed. Our work is based on a rigorous mathematical proof of privacy and utility for the general binary RR mechanism, and numerical verification illustrates the correctness of the conclusions. It can provide theoretical support for the design of binary RR mechanism and can be applied in data collection, analysis and publishing.

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Density Clustering Algorithm Based on KD-Tree and Voting Rules

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computers, Materials & Continua 2024年第5期79卷 3239-3259页

作者： Hui Du Zhiyuan Hu Depeng Lu Jingrui Liu The college of computer science and Engineering Northwest Normal UniversityLanzhou730070China

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

关键词： Density peaks clustering KD-Tree K-nearest neighbors voting rules

Model Checking Computation Tree Logic over Multi-Valued Decision Processes and Its Reduction Techniques

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Chinese Journal of Electronics 2024年

作者： Wuniu LIU Junmei WANG Qing HE Yongming LI The School of Mathematics and Statistics Shaanxi Normal University The School of computer science Shaanxi Normal University

Model checking computation tree logic(CTL) based on multi-valued possibility measures has been studied by Li et al. in 2019. However, the previous work did not consider the nondeterministic choices inherent in systems represented by multi-valued Kripke structures(MvKSs). This nondeterminism is crucial for accurate system modeling, decision making, and control capabilities. To address this limitation, we draw inspiration from the generalization of Markov chains(MCs) to Markov decision processes(MDPs) in probabilistic systems. By integrating nondeterminism into MvKS, we introduce multi-valued decision processes(MvDPs) as a framework for modeling MvKSs with nondeterministic choices. We investigate the challenges of model checking over MvDPs. Verifying properties are expressed by using multi-valued computation tree logic(MvCTL) based on schedulers. Our primary objective is to leverage fixpoint techniques to determine the maximum and minimum possibilities of the system satisfying temporal properties. This allows us to identify the optimal or worst-case schedulers for decision making or control *** aim to develop reduction techniques that enhance the efficiency of model checking, thereby reducing the associated time complexity.

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A Deep Deterministic Policy Gradient-Based Method for Enforcing Service Fault-Tolerance in MEC

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Chinese Journal of Electronics 2024年第4期33卷 899-909页

作者： Tingyan LONG Peng CHEN Yunni XIA Yong MA Xiaoning SUN Jiale ZHAO Yifei LYU college of computer science Chongqing University School of computer and Software Engineering Xihua University School of computer and Information Engineering Jiangxi Normal University School of computer and Information science Chongqing Normal University

Mobile edge computing(MEC) provides edge services to users in a distributed and on-demand *** to the heterogeneity of edge applications, deploying latency and resource-intensive applications on resourceconstrained devices is a key challenge for service providers. This is especially true when underlying edge infrastructures are fault and error-prone. In this paper, we propose a fault tolerance approach named DFGP, for enforcing mobile service fault-tolerance in MEC. It synthesizes a generative optimization network(GON) model for predicting resource failure and a deep deterministic policy gradient(DDPG) model for yielding preemptive migration *** show through extensive simulation experiments that DFGP is more effective in fault detection and guaranteeing quality of service, in terms of fault detection accuracy, migration efficiency, task migration time, task scheduling time,and energy consumption than other existing methods.

关键词： Fault tolerance Multi-access edge computing Processor scheduling Fault detection Image edge detection Fault tolerant systems Quality of service