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  • Open Access


    Optimizing Spatial Relationships in GCN to Improve the Classification Accuracy of Remote Sensing Images

    Zimeng Yang, Qiulan Wu, Feng Zhang*, Xuefei Chen, Weiqiang Wang, Xueshen Zhang

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 491-506, 2023, DOI:10.32604/iasc.2023.037558

    Abstract Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation. With the continuous development of artificial intelligence technology, the use of deep learning methods for interpreting remote-sensing images has matured. Existing neural networks disregard the spatial relationship between two targets in remote sensing images. Semantic segmentation models that combine convolutional neural networks (CNNs) and graph convolutional neural networks (GCNs) cause a lack of feature boundaries, which leads to the unsatisfactory segmentation of various target feature boundaries. In this paper, we propose a new semantic segmentation model for remote sensing images (called DGCN hereinafter),… More >

  • Open Access


    Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

    Xiaorui Zhang1,2,3,*, Feng Xu2, Wei Sun3,4, Yan Jiang2, Yi Cao5

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1695-1709, 2023, DOI:10.32604/csse.2023.031506

    Abstract 3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one perspective. When existing methods reconstruct the mesh surface of complex objects, the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework; the 3D topology is easily limited by predefined templates and inflexible, and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology, thus destroying the surface details; the training of the reconstruction network is limited by the large amount of information attached… More >

  • Open Access


    Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

    Binhui Tang1,3, Junfeng Wang2,*, Huanran Qiu3, Jian Yu2, Zhongkun Yu2, Shijia Liu2,4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 235-252, 2023, DOI:10.32604/cmc.2023.029135

    Abstract The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats (APT). Extracting attack behaviors, i.e., Tactics, Techniques, Procedures (TTP) from Cyber Threat Intelligence (CTI) can facilitate APT actors’ profiling for an immediate response. However, it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature. Based on the Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) of threat behavior description, this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network (HTN) and Graph Convolutional Network (GCN) to solve this… More >

  • Open Access


    Correlating Transcriptional Networks to Papillary Renal Cell Carcinoma Survival: A Large-Scale Coexpression Analysis and Clinical Validation

    Xingliang Feng*1, Meng Zhang*†1, Jialin Meng*, Yongqiang Wang, Yi Liu*, Chaozhao Liang*, Song Fan*

    Oncology Research, Vol.28, No.3, pp. 285-297, 2020, DOI:10.3727/096504020X15791676105394

    Abstract We aimed to investigate the potential mechanisms of progression and identify novel prognosis-related biomarkers for papillary renal cell carcinoma (PRCC) patients. The related data were derived from The Cancer Genome Atlas (TCGA) and then analyzed by weighted gene coexpression network analysis (WGCNA). The correlation between each module and the clinical traits were analyzed by Pearson’s correlation analysis. Pathway analysis was conducted to reveal potential mechanisms. Hub genes within each module were screened by intramodule analysis, and visualized by Cytoscape software. Furthermore, important hub genes were validated in an external dataset and clinical samples. A total of 5,839 differentially expressed genes… More >

  • Open Access


    Identification of a Novel Cancer Stemness-Associated ceRNA Axis in Lung Adenocarcinoma via Stemness Indices Analysis

    Pihua Han*†1, Haiming Yang‡1, Xiang Li*1, Jie Wu*, Peili Wang§, Dapeng Liu*, Guodong Xiao, Xin Sun*, Hong Ren*

    Oncology Research, Vol.28, No.7-8, pp. 715-729, 2020, DOI:10.3727/096504020X16037124605559

    Abstract The aim of this study was to identify a novel cancer stemness-related ceRNA regulatory axis in lung adenocarcinoma (LUAD) via weighted gene coexpression network analysis of a stemness index. The RNA sequencing expression profiles of 513 cancer samples and 60 normal samples were obtained from the TCGA database. Differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs) were identified with R software. Functional enrichment analysis was conducted using DAVID 6.8. The ceRNA network was constructed via multiple bioinformatics analyses, and the correlations between possible ceRNAs and prognosis were analyzed using Kaplan–Meier plots. WGCNA was then applied to distinguish key genes… More >

  • Open Access


    Weighted gene co-expression network analysis identifies a novel immune-related gene signature and nomogram to predict the survival and immune infiltration status of breast cancer


    BIOCELL, Vol.46, No.7, pp. 1661-1673, 2022, DOI:10.32604/biocell.2022.018023

    Abstract Breast cancer is one of the most common cancers in the world and seriously threatens the health of women worldwide. Prognostic models based on immune-related genes help to improve the prognosis prediction and clinical treatment of breast cancer patients. In the study, we used weighted gene co-expression network analysis to construct a co-expression network to screen out highly prognostic immune-related genes. Subsequently, the prognostic immune-related gene signature was successfully constructed from highly immune-related genes through COX regression and LASSO COX analysis. Survival analysis and time receiver operating characteristic curves indicate that the prognostic signature has strong predictive performance. And we… More >

  • Open Access


    Molecular mechanisms of Tanshinone IIA in Hepatocellular carcinoma therapy via WGCNA-based network pharmacology analysis


    BIOCELL, Vol.46, No.5, pp. 1245-1259, 2022, DOI:10.32604/biocell.2022.018117

    Abstract Hepatocellular carcinoma (HCC) is a worldwide malignant tumor that caused irreversible consequences. Tanshinone IIA has been shown to play a notable role in HCC treatment. However, the potential targets and associating mechanism of Tanshinone IIA against HCC remain unknown. We first screened out 105 overlapping genes by integrating the predicted targets of Tanshinone IIA from multiple databases and the differentially expressed genes of HCC from the Cancer Genome Atlas (TCGA) database. Then, we performed weighted gene co-expression network analysis (WGCNA) using the RNA-seq profiles of overlapping genes and HCC-related clinical information. 23 genes related to clinical tumor grade in the… More >

  • Open Access


    WGCNA and LASSO algorithm constructed an immune infiltration-related 5-gene signature and nomogram to improve prognosis prediction of hepatocellular carcinoma


    BIOCELL, Vol.46, No.2, pp. 401-415, 2022, DOI:10.32604/biocell.2022.016989

    Abstract Hepatocellular carcinoma (HCC) is a common immunogenic malignant tumor. Although the new strategies of immunotherapy and targeted therapy have made considerable progress in the treatment of HCC, the 5-year survival rate of patients is still very low. The identification of new prognostic signatures and the exploration of the immune microenvironment are crucial to the optimization and improvement of molecular therapy strategies. We studied the potential clinical benefits of the inflammation regulator miR-93-3p and mined its target genes. Weighted gene co-expression network analysis (WGCNA), univariate and multivariate COX regression and the LASSO COX algorithm are employed to identify prognostic-related genes and… More >

  • Open Access


    The F5 gene predicts poor prognosis of patients with gastric cancer by promoting cell migration identified using a weighted gene co-expression network analysis

    MENGYI TANG1,2,3,4,#, BOWEN YANG1,2,3,4,#, CHUANG ZHANG1,2,3,4, CHAOXU ZHANG1,2,3,4, DAN ZANG1,2,3,4, LIBAO GONG1,2,3,4, YUNPENG LIU1,2,3,4, ZHI LI1,2,3,4,*, XIUJUAN QU1,2,3,4,*

    BIOCELL, Vol.45, No.4, pp. 911-921, 2021, DOI:10.32604/biocell.2021.010119

    Abstract Distal gastric cancer (DGC) is a subgroup of gastric cancer (GC), which has different molecular characteristics from proximal gastric cancer (PGC). These differences result in different overall survival (OS) rates; however, data pertaining to the survival rate in PGC or DGC are contradictory. This suggests that the location of GC is not the unique cause of the different survival rates, while the molecular characteristics might be more important factors determining the prognosis of DGC. Therefore, the aim of this study was to discover key prognostic factors in DGC using bioinformatic methods and to explore the potential molecular mechanism. The Cancer… More >

  • Open Access


    Semi-GSGCN: Social Robot Detection Research with Graph Neural Network

    Xiujuan Wang1, Qianqian Zheng1, *, Kangfeng Zheng2, Yi Sui1, Jiayue Zhang1

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 617-638, 2020, DOI:10.32604/cmc.2020.011165

    Abstract Malicious social robots are the disseminators of malicious information on social networks, which seriously affect information security and network environments. Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks. Supervised classification based on manual feature extraction has been widely used in social robot detection. However, these methods not only involve the privacy of users but also ignore hidden feature information, especially the graph feature, and the label utilization rate of semi-supervised algorithms is low. Aiming at the problems of shallow feature extraction and low label utilization rate in existing social network robot detection… More >

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