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Drug repositioning based on residual attention network and free multiscale adversarial training

BMC BIOINFORMATICS 2024年第1期25卷 1-21页

作者： Li, Guanghui Li, Shuwen Liang, Cheng Xiao, Qiu Luo, Jiawei East China Jiaotong Univ Sch Informat Engn Nanchang Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China

BackgroundConducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently *** paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease *** comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor

关键词： graph attention network Residual network graph autoencoder Adversarial training Drug-disease association

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TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第2期35卷 2003-2013页

作者： Wu, Zonghan Zheng, Da Pan, Shirui Gan, Quan Long, Guodong Karypis, George Univ Technol Sydney FEIT Ctr Artificial Intelligence Ultimo NSW 2007 Australia Amazon Seattle WA 98109 USA Monash Univ Dept Data Sci & AI Clayton Vic 3800 Australia Griffith Univ Sch Informat & Commun Technol Southport Qld 4222 Australia

This article aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data. For spatial-temporal attribute entities with topological structure, the space-time is consecutive and unified while each node's current status is influenced by its neighbors' past states over variant periods of each neighbor. Most spatial-temporal neural networks for traffic forecasting study spatial dependency and temporal correlation separately in processing, gravely impaired the spatial-temporal integrity, and ignore the fact that the neighbors' temporal dependency period for a node can be delayed and dynamic. To model this actual condition, we propose TraverseNet, a novel spatial-temporal graph neural network, viewing space and time as an inseparable whole, to mine spatial-temporal graphs while exploiting the evolving spatial-temporal dependencies for each node via message traverse mechanisms. Experiments with ablation and parameter studies have validated the effectiveness of the proposed TraverseNet, and the detailed implementation can be found from https://***/nnzhan/TraverseNet.

关键词： Convolution Message passing Correlation Kernel graph neural networks Forecasting Convolutional neural networks Deep learning graph autoencoder graph convolutional networks graph neural networks (GNNs) graph representation learning network embedding

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A Novel Group Recommendation Model With Two-Stage Deep Learning

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第9期52卷 5853-5864页

作者： Huang, Zhenhua Liu, Yajun Zhan, Choujun Lin, Chen Cai, Weiwei Chen, Yunwen South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China Xiamen Univ Sch Informat Xiamen 361000 Peoples R China Cent South Univ Forestry & Technol Sch Logist & Transportat Changsha 410004 Peoples R China No Arizona Univ Grad Sch Flagstaff AZ 86011 USA DataGrand Inc Res & Dev Dept Shenzhen 518063 Peoples R China

Group recommendation has recently drawn a lot of attention to the recommender system community. Currently, several deep learning-based approaches are leveraged to learn preferences of groups for items and predict next items in which groups may be interested. Yet, their recommendation performance is still unsatisfactory due to sparse group-item interactions. To address this challenge, this study presents a novel model, called group recommendation model with two-stage deep learning (GRMTDL), which encompasses two sequential stages: 1) group representation learning (GRL) and 2) group preference learning (GPL). In GRL, we first construct an undirected tripartite graph over group-user-item interactions, and then employ it to accurately learn group semantic features through a spatial-based variational graph autoencoder network. While in GPL, we first introduce a dual PL-network that contains two structure-sharing subnetworks: 1) group PL-network employed for GPL and 2) user PL-network utilized for user preference learning. Then, we design a novel layered transfer learning (LTL) method to learn group preferences by alternately optimizing these two subnetworks. In particular, it can effectively absorb knowledge of user preferences into the process of GPL. Furthermore, extensive experiments on four real-world datasets demonstrate that the proposed GRMTDL model outperforms the state-of-the-art baselines for group recommendation.

关键词： Semantics Deep learning Task analysis Decision making Training Recommender systems Representation learning Deep learning graph autoencoder group recommendation knowledge transferring representation learning

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Ensemble Clustering With Attentional Representation

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第2期36卷 581-593页

作者： Hao, Zhezheng Lu, Zhoumin Li, Guoxu Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence Sch Cybersecur Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Key Lab Intelligent Interact & Applicat Sch Artificial Intelligence Opt & Elect iOPENMini Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Key Lab Intelligent Interactionand Applicat Xian 710072 Shaanxi Peoples R China

Ensemble clustering has emerged as a powerful framework for analyzing heterogeneous and complex data. Despite the abundance of existing schemes, co-association matrix-based methods remain the mainstream approach. However, focusing solely on pairwise correlations falls short of fully capturing the intricate cluster relationships. Moreover, despite its potential, ensemble clustering has yet to effectively leverage the powerful representation capabilities of neural networks. To address these limitations, we propose a deep ensemble clustering method called Ensemble Clustering with Attentional Representation (ECAR). Our method considers the results of base partitions as groups with related information to explore higher-order fusion information. ECAR captures the importance of each sample's association with its related group by employing an attentional network, and encodes this information into a low-dimensional representation. The attentional network is trained by jointly optimizing the clustering loss from soft assignments learned from the embeddings and the reconstruction loss from the weighted graph generated from ensemble clustering. During training, the weights of base partitions are adaptively refined to promote diversity and consistency while reducing the impact of low-quality and redundant base partitions. Extensive experimental results on real-world datasets demonstrate the substantial improvement of our method over existing baseline ensemble clustering methods and deep clustering methods.

关键词： Deep clustering ensemble clustering graph autoencoder self-attentional unsupervised representation learning

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Unsupervised graph Embedding via Adaptive graph Learning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第4期45卷 5329-5336页

作者： Zhang, Rui Zhang, Yunxing Lu, Chengjun Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China

graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. However, the performance of GAEs is very dependent on the quality of the graph structure, i.e., of the adjacency matrix. In other words, GAEs would perform poorly when the adjacency matrix is incomplete or be disturbed. In this paper, two novel unsupervised graph embedding methods, unsupervised graph embedding via adaptive graph learning (BAGE) and unsupervised graph embedding via variational adaptive graph learning (VBAGE) are proposed. The proposed methods expand the application range of GAEs on graph embedding, i.e, on the general datasets without graph structure. Meanwhile, the adaptive learning mechanism can initialize the adjacency matrix without being affected by the parameter. Besides that, the latent representations are embedded with the Laplacian graph structure to preserve the topology structure of the graph in the vector space. Moreover, the adjacency matrix can be self-learned for better embedding performance when the original graph structure is incomplete. With adaptive learning, the proposed method is much more robust to the graph structure. Experimental studies on several datasets validate our design and demonstrate that our methods outperform baselines by a wide margin in node clustering, node classification, link prediction, and graph visualization tasks.

关键词： Laplace equations graph neural networks Adaptation models Adaptive learning Task analysis Decoding Topology graph embedding adaptive graph learning graph autoencoder

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BEDetector: A Two-Channel Encoding Method to Detect Vulnerabilities Based on Binary Similarity

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IEEE ACCESS 2021年 9卷 51631-51645页

作者： Yu, Lu Lu, Yuliang Shen, Yi Huang, Hui Zhu, Kailong Natl Univ Def Technol Coll Elect Engn Hefei 230007 Peoples R China Anhui Prov Key Lab Cyberspace Secur Situat Awaren Hefei 230007 Peoples R China

Applying neural network technology to binary similarity detection has become a promising search topic, and vulnerability detection is an important application field of binary similarity detection. When embedding binary code into matrix by neural network, the problem of feature representation also needs to be solved in vulnerability detection. However, most of the current researches extract the syntax or structural features of binary code, and take basic block as the minimum analysis unit, which is relatively coarse. In addition, the structural features of binary functions are usually represented by the dependency graph. In the embedding process, only the neighbour information of the node can be obtained, ignoring the global information of the graph. To solve these two problems, we propose a two-channel feature extraction method to obtain semantic feature in finer granularity and represent the structural features globally instead of locally. Inspired by natural language process, we propose a contextual semantic feature extraction method to obtain different granularity features of binary functions. It takes instruction as the minimum analysis unit and obtains the semantic relationship between instructions. Meanwhile, in order to represent the structural feature of each function, we propose a neural GAE model instead of the widely used structure2vec model. In this way, we can preserve and reconstruct the control dependencies between the basic blocks in the whole graph. We have implemented a prototype system BEDetector, evaluated the effectiveness of its neural model and compared the accuracy of vulnerability function detection with state-of-the-art system. Besides, we choose the real-world firmware files as the detection target and prove that BEDetector can achieve a relatively high detection rate. BEDetector could reach a precision of 88.8%, 86.7% and 100% when ranking top-50 candidate functions in the detection of the CVE vulnerability function ssl 3_get_key_exchange

关键词： Semantic analysis binary code embedding graph autoencoder binary similarity detection

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Adversarially regularized joint structured clustering network

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INFORMATION SCIENCES 2022年 615卷 136-151页

作者： Yang, Yachao Ju, Fujiao Sun, Yanfeng Gao, Junbin Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing Key Lab Multimedia & Intelligent Software Beijing 100124 Peoples R China Univ Sydney Business Sch Discipline Business Analyt Sydney NSW 2006 Australia

Deep clustering has achieved great success as its powerful ability to learn effective repre-sentation. Especially, graph network clustering has attracted more and more attention. Considering the great success of graph autoencoder (GAE) in encoding the graph structure and Deep autoencoder (DAE) in extracting valuable representations from the data itself, in this paper, we construct an Adversarially regularized Joint Structured Clustering Network (AJSCN) by integrating GAE and DAE. The framework links GAE and DAE together by trans-ferring the representation learned by DAE to the corresponding layer of GAE to alleviate the over-smoothing problem. Furthermore, the latent representation learned by GAE is enforced to match a prior distribution via an adversarial training scheme to avoid the free of any structure of latent space. We design a joint supervision mechanism to improve the clustering performance consisting of self-supervision and mutual supervision. The self -supervision is to learn more compact representations, and mutual-supervision makes dif-ferent representations more consistent. Experiment results demonstrate the superiority of the proposed model against the state-of-the-art algorithms and achieve significant improvement on six benchmark datasets. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Deep clustering Deep autoencoder graph autoencoder Adversarial training Self-supervision

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scGAMNN: graph Antoencoder-Based Single-Cell RNA Sequencing Data Integration Algorithm Using Mutual Nearest Neighbors

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第11期27卷 5665-5674页

作者： Zhang, Bai Wu, Hanwen Wang, Yan Xuan, Chenxu Gao, Jie Jiangnan Univ Sch Sci Wuxi 214122 Jiangsu Peoples R China

It is critical to correctly assemble high-dimensional single-cell RNA sequencing (scRNA-seq) datasets and downscale them for downstream analysis. However, given the complex relationships between cells, it remains a challenge to simultaneously eliminate batch effects between datasets and maintain the topology between cells within each dataset. Here, we propose scGAMNN, a deep learning model based on graph autoencoder, to simultaneously achieve batch correction and topology-preserving dimensionality reduction. The low-dimensional integrated data obtained by scGAMNN can be used for visualization, clustering and trajectory *** comparing it with the other five methods, multiple tasks show that scGAMNN consistently has comparable data integration performance in clustering and trajectory conservation.

关键词： Batch correction deep learning graph autoencoder mutual nearest neighbor scRNA-seq data integration

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GAE-Based Document Embedding Method for Clustering

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IEEE ACCESS 2022年 10卷 130089-130096页

作者： Jung, Sungwon Ka, Sangmin Sogang Univ Dept Comp Sci & Engn Seoul 121742 South Korea

Document embedding methods for clustering using deep neural networks have been proposed recently. However, the existing deep neural network-based document embedding methods for clustering have a problem of either generating document embeddings dependent on a given number of document clusters or generating document embeddings that do not take into account the characteristic of high similarity between documents belonging to the same document cluster. In this paper, we propose a new document embedding method for clustering by using a graph autoencoder. To this end, we construct an undirected and weighted sparse graph from a set of documents wherein each document is represented by a node, and all the weighted edges created in the graph have high cosine similarities between the two end nodes. We then apply the proposed graph autoencoder to the graph to compute node embedding vectors. Each node embedding vector in the graph is used as a document embedding vector. This paper presents in-depth experimental analyses of the proposed method. Experimental results on various real document data sets demonstrate that the proposed approach affords the significant performance improvement over the existing document embedding methods.

关键词： Document embedding text embedding document clustering graph autoencoder graph CNN autoencoder deep neural network

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An Integrated Method Based on Wasserstein Distance and graph for Cancer Subtype Discovery

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第6期20卷 3499-3510页

作者： Cao, Qingqing Zhao, Jianping Wang, Haiyun Guan, Qi Zheng, Chunhou Xinjiang Univ Coll Math & Syst Sci Urumqi 830046 Peoples R China Xinjiang Univ Inst Math & Phys Urumqi 830046 Peoples R China Anhui Univ Coll Comp Sci & Technol Hefei 230039 Peoples R China

Due to the complexity of cancer pathogenesis at different omics levels, it is necessary to find a comprehensive method to accurately distinguish and find cancer subtypes for cancer treatment. In this paper, we proposed a new cancer multi-omics subtype identification method, which is based on variational autoencoder measured by Wasserstein distance and graph autoencoder (WVGMO). This method depends on two foremost models. The first model is a variational autoencoder measured by Wasserstein distance (WVAE), which is used to extract potential spatial information of each omic data type. The second model is the graph autoencoder (GAE) with the second-order proximity. It has the capability to retain the topological structure information and feature information of the multi-omics data. And then, the identification of cancer subtypes via k-means clustering. Extensive experiments were conducted on seven different cancers based on four omics data from TCGA. The results show that WVGMO provides equivalent or even better results than the most of advanced synthesis methods.

关键词： Multi-omics data clustering wasserstein distance cancer data graph autoencoder

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