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MGDRGCN: A novel framework for predicting metabolite-disease connections using tripartite network and relational graph convolutional network

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JOURNAL OF COMPUTATIONAL SCIENCE 2025年 85卷

作者： Lu, Pengli Li, Ling Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Gansu Peoples R China

Metabolites are fundamental to the existence of biomolecules, and numerous studies have demonstrated that uncovering the connections between metabolites and diseases can enhance our understanding of disease pathogenesis. Traditional biological methods can identify potential metabolite-disease relationships, but these approaches often require significant human and material resources. Consequently, computational methods have emerged as amore efficient alternative. However, most computational methods primarily rely on metabolite- disease associations and rarely explore the impact of more biological entities. To address this issue, we propose a novel computational framework based on a metabolite-gene-disease tripartite heterogeneous network and relational graph convolutional network (R-GCN), abbreviated as MGDRGCN. Specifically, we construct three types of similarity networks from multiple data sources, including metabolite and gene functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity for metabolites and diseases. Next, we use principal component analysis to further extract features and construct a tripartite heterogeneous network with genes as the bridge. This network structure comprehensively captures and represents the complex relationships among metabolites, genes and diseases. We employ R-GCN to extract higher-order information from the tripartite heterogeneous network. Finally, we input the embeddings learned from R-GCN into a residual network classifier to predict potential metabolite-disease associations. In five-fold cross-validation experiments, MGDRGCN exhibit outstanding performance, with both AUC (0.9866) and AUPR (0.9865) significantly surpassing other advanced methods. Additionally, case studies further demonstrate MGDRGCN's superior performance in predicting metabolite-disease associations. Overall, the introduction of MGDRGCN provides new perspectives and methods for future biomedical research, offering p

关键词： Metabolite-disease relationships Gene Principal component analysis relational graph convolutional network

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Segmentation-aware relational graph convolutional network with multi-layer CRF for nested named entity recognition

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COMPLEX & INTELLIGENT SYSTEMS 2024年第6期10卷 7893-7905页

作者： Han, Daojun Wang, Zemin Li, Yunsong Ma, Xiangbo Zhang, Juntao Henan Univ Henan Engn Res Ctr Intelligent Technol & Applicat Kaifeng 475000 Peoples R China Henan Univ Sch Comp & Informat Engn Kaifeng 475000 Peoples R China Beijing Wan Wei Sheng New Technol Co Ltd Beijing 102200 Peoples R China

Named Entity Recognition (NER) is fundamental in natural language processing, involving identifying entity spans and types within a sentence. Nested NER contains other entities, which pose a significant challenge, especially pronounced in the domain of medical-named entities due to intricate nesting patterns inherent in medical terminology. Existing studies can not capture interdependencies among different entity categories, resulting in inadequate performance in nested NER tasks. To address this problem, we propose a novel Layer-based architecture with Segmentation-aware relational graph convolutional network (LSRGCN) for Nested NER in the medical domain. LSRGCN comprises two key modules: a shared segmentation-aware encoder and a multi-layer conditional random field decoder. The former part provides token representation including boundary information from sentence segmentation. The latter part can learn the connections between different entity classes and improve recognition accuracy through secondary decoding. We conduct experiments on four datasets. Experimental results demonstrate the effectiveness of our model. Additionally, extensive studies are conducted to enhance our understanding of the model and its capabilities.

关键词： Named entity recognition Shared segmentation-aware encoder relational graph convolutional network Multi-layer conditional random field

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Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism

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BRIEFINGS IN BIOINFORMATICS 2022年第6期23卷 bbac403-bbac403页

作者： Zhang, Peng Tu, Shikui Zhang, Wen Xu, Lei Shanghai Jiao Tong Univ SJTU Dept Comp Sci & Engn Shanghai Peoples R China Huazhong Agr Univ Coll Informat Wuhan Peoples R China Shanghai Jiao Tong Univ SJTU Comp Sci & Engn Dept Shanghai Peoples R China Shanghai Jiao Tong Univ SJTU Brain Sci Tech Res Ctr Shanghai Peoples R China

Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did not perform well by building model for each cell line independently. In this paper, we present a novel encoder-decoder network named SDCNet for predicting cell line-specific SDCs. SDCNet learns common patterns across different cell lines as well as cell line-specific features in one model for drug combinations. This is realized by considering the SDC graphs of different cell lines as a relational graph, and constructing a relational graph convolutional network (R-GCN) as the encoder to learn and fuse the deep representations of drugs for different cell lines. An attention mechanism is devised to integrate the drug features from different layers of the R-GCN according to their relative importance so that representation learning is further enhanced. The common patterns are exploited through partial parameter sharing in cell line-specific decoders, which not only reconstruct the known SDCs but also predict new ones for each cell line. Experiments on various datasets demonstrate that SDCNet is superior to state-of-the-art methods and is also robust when generalized to new cell lines that are different from the training ones. Finally, the case study again confirms the effectiveness of our method in predicting novel reliable cell line-specific SDCs.

关键词： drugs synergistic drug combinations attention mechanism relational graph convolutional network cancer treatment

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SedSVD: Statement-level software vulnerability detection based on relational graph convolutional network with subgraph embedding

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INFORMATION AND SOFTWARE TECHNOLOGY 2023年第1期158卷

作者： Dong, Yukun Tang, Yeer Cheng, Xiaotong Yang, Yufei Wang, Shuqi China Univ Petr East China Qingdao Inst Software Coll Comp Sci & Technol Dongying Shandong Peoples R China

Context: Current deep-learning based vulnerability detection methods have been proven more automatic and correct to a certain extent, nonetheless, they are limited to detect at function-level or file-level, which can hinder software developers from acquiring more detailed information and conducting more targeted repairs. graph-based detection methods have shown dominant performance over others. Unfortunately, the information they reveal has not been fully ***: We design SedSVD (Subgraph embedding driven Statement-level Vulnerability Detection) with two objectives: (i) to better utilize the information the code-related graphs can reflect;(ii) to detect vulnerabilities at a finer-grained ***: In our work, we propose a novel graph-based detection framework that embeds graphs at subgraph-level to realize statement-level detection. It first leverages Code Property graph (CPG) to learn both semantic and syntactic information from source code, and then selects several center nodes (code elements) in CPG to build their subgraphs. After embedding each subgraph with its nodes and edges, we apply relational graph convolutional network (RGCN) to process different edges differently. A Multi-Layer Perceptron (MLP) layer is further added to ensure its prediction ***: We conduct our experiments on C/C++ projects from NVD and SARD. Experimental results show that SedSVD achieves 95.15% in F1-measure which proves our work to be more ***: Our work detects at a finer-grained level and achieves higher F1-measure than existing state-of-art vulnerability detection techniques. Besides, we provide a more detailed detection report pointing the specific error code elements within statements.

关键词： Software vulnerability detection Code property graph relational graph convolutional network Subgraph embedding Statement-level detection

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Multi-Task Prediction Method Based on GGCN for Object Centric Event Logs

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IEEE ACCESS 2025年 13卷 53949-53963页

作者： Ke, Li Huan, Fang Xu, Yifei Shao, Chifeng Anhui Univ Sci & Technol Sch Math & Big Data Huainan 232001 Peoples R China

Event logs constitute the fundamental data for predictive process monitoring research, and the quality and format of these logs are crucial for predictive analysis. Existing process prediction methods are primarily based on flattened event logs, which overlook multi-object interactions and complex dependencies, thereby limiting their ability to model complex processes. In contrast, object-centric event logs associate events with multiple objects, thereby enabling the modeling of multi-object interactions and complex dependencies within processes. Object-centric event logs frequently employ graph neural networks for predictive analysis. To systematically analyze and compare the performance of existing predictive models designed for object centric event logs, this paper proposes a multi-task prediction model, graph-based relational graph convolutional network (GGCN), which is based on relational graph convolutional networks and gated recurrent units. The proposed GGCN model embeds the multi-relational aspects of object-centric event logs into a graph structure and inputs this structure into the model, thereby achieving three tasks: next activity prediction, next timestamp prediction, and remaining time prediction. The graph embedding structure of the GGCN model is capable of extracting complex multi-relational associations and temporal dependency features between events. Finally, we conducted multi-task process prediction experiments using four publicly available datasets. The results demonstrate that, compared with baseline models such as graph convolutional networks (GCN), graph Isomorphism networks (GIN), and graph Attention networks (GAT), the proposed GGCN model exhibits superior predictive performance and data robustness. Furthermore, in relation to the impact of subgraph size in graph embeddings on prediction performance, the experimental results indicate a significant correlation between subgraph size and model performance.

关键词： Object-centric event logs relational graph convolutional network gated recurrent units multi-task process prediction Object-centric event logs relational graph convolutional network gated recurrent units multi-task process prediction

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graphFVD: Property graph-based fine-grained vulnerability detection

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COMPUTERS & SECURITY 2025年 151卷

作者： Shao, Miaomiao Ding, Yuxin Cao, Jing Li, Yilin Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518000 Guangdong Peoples R China

Deep learning technology can automatically extract features from software source code, making it widely used for detecting software vulnerabilities. Most existing deep learning-based approaches rely on whole functions or sequence-level program slices to identify vulnerabilities. However, these approaches often struggle to capture comprehensive vulnerability semantics, leading to high false positive rates and false negative rates. In this paper, we propose graphFVD, a novel property graph-based fine-grained vulnerability detection approach. Our approach extracts property graph-based slices from the Code Property graph and introduces a Hierarchical Attention graph convolutional network to learn graph embeddings. graphFVD provides a fine-grained code representation that captures syntax, control flow, data flow, and the natural sequential order of source code relevant to vulnerabilities. We evaluate the effectiveness of our approach on two real-world vulnerability datasets. Experimental results demonstrate that our approach outperforms existing state-of-the-art vulnerability detection methods on both datasets.

关键词： Vulnerability detection Program slicing Deep learning Hierarchical attention relational graph convolutional network

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Malicious software identification based on deep learning algorithms and API feature extraction

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EURASIP JOURNAL ON INFORMATION SECURITY 2025年第1期2025卷 1-15页

作者： Sun, Wei Heilongjiang Polytech Sch Informat Engn Harbin 150070 Heilongjiang Peoples R China

With the popularization of mobile Internet, the Android operating system has become the main target of malware attacks because of its openness. Traditional malware detection methods face challenges in handling complex feature representations, especially in utilizing the semantic information and call order of application programming interface call sequences. Therefore, this study develops a deep learning method to identify malicious software by analyzing the application programming interface calls and constructing heterogeneous graphs of Android applications. The results showed that the proposed method achieved accuracies of 92.80% and 94.24% on the Drebin and AndroZoo datasets, demonstrating excellent robustness and generalization ability. The ablation experiment showed that the accuracy of the complete model was 94.71%, verifying the key role of each part of the method. In comparison with existing methods, the proposed method led with an average accuracy of 94.27%, while maintaining detection time within 5-10 s, demonstrating high efficiency and practicality. This study contributes to the in-depth exploration of semantic information and behavioral patterns of application programming interface call sequences. The efficient malware identification method developed can cope with the constantly evolving malware threats.

关键词： Malicious software detection Deep learning algorithms Application programming interface Feature extraction techniques relational graph convolutional network

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A Controllability Reinforcement Learning Method for Pancreatic Cancer Biomarker Identification

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2024年第4期23卷 556-563页

作者： Wang, Yan Hong, Jie Lu, Yuting Sheng, Nan Fu, Yuan Yang, Lili Meng, Lingyu Huang, Lan Wang, Hao Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Aberystwyth Univ Sch Environm & Rural Sci Aberystwyth SY23 3FL England Jilin Univ Dept Obstet Hosp 1 Changchun 130031 Peoples R China Jilin Univ Dept Hepatobiliary & Pancreat Surg Hosp 1 Changchun 130031 Peoples R China Harbin Med Univ Dept Hepatopancreatobiliary Surg Affiliated Hosp 2 Harbin 150001 Peoples R China

Pancreatic cancer is one of the most malignant cancers with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding new biomarkers for pancreatic cancer. Many network-based methods used to identify cancer biomarkers are proposed, among which the combination of network controllability appears. However, most of the existing methods do not study RNA, rely on priori and mutations information, or can only achieve classification tasks. In this study, we propose a method combined relational graph convolutional network and Deep Q-network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. Firstly, we construct a regulation network containing long non-coding RNA, microRNA, and messenger RNA. Secondly, relational graph convolutional network is used to learn the node representation. Finally, we use the idea of Deep Q-network to build a model, which score and prioritize each RNA with the Popov-Belevitch-Hautus criterion. We train RDDriver on three small simulated networks, and calculate the average score after applying the model parameters to the regulation networks separately. To demonstrate the effectiveness of the method, we perform experiments for comparison between RDDriver and other eight methods based on the approximate benchmark of three types cancer drivers RNAs.

关键词： RNA Pancreatic cancer Controllability Benchmark testing Biomarkers graph convolutional networks Reinforcement learning Pancreatic cancer biomarkers transcriptional regulation network relational graph convolutional network Popov-Belevitch-Hautus criterion

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More Than Syntaxes: Investigating Semantics to Zero-shot Cross-lingual Relation Extraction and Event Argument Role Labelling

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2024年第5期23卷 1-21页

作者： Wei, Kaiwen Jin, Li Zhang, Zequn Guo, Zhi Li, Xiaoyu Liu, Qing Feng, Weimiao Aerosp Informat Res Inst Beijing 100190 Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

Syntactic dependency structures are commonly utilized as language-agnostic features to solve the word order difference issues in zero-shot cross-lingual relation and event extraction tasks. However, while sentences in multiple forms can be employed to express the same meaning, the syntactic structure may vary considerably in specific scenarios. To fix this problem, we find semantics are rarely considered, which could provide a more consistent semantic analysis of sentences and be served as another bridge between different languages. Therefore, in this article, we introduce Syntax and Semantic Driven network (SSDN) to equip syntax and semantic knowledge across languages simultaneously. Specifically, predicate-argument structures from semantic role labelling are explicitly incorporated into word representations. Then, a semantic-aware relational graph convolutional network and a transformer-based encoder are utilized to model both semantic dependency and syntactic dependency structures, respectively. Finally, a fusion module is introduced to integrate output representations adaptively. We conduct experiments on the widely used Automatic Content Extraction 2005 English, Chinese, and Arabic datasets. The evaluation results demonstrate that the proposed method achieves the state-of-the-art performance. Further study also indicates SSDN could produce robust representations that facilitate the transfer operations across languages.

关键词： Cross-lingual relation and event extraction zero-resource transfer semantic parsing relational graph convolutional network

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BotCL: a social bot detection model based on graph contrastive learning

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第9期66卷 5185-5202页

作者： Li, Yan Li, Zhenyu Gong, Daofu Hu, Qian Lu, Haoyu Henan Prov Key Lab Cyberspace Situat Awareness Zhengzhou Henan Peoples R China

The proliferation of social bots on social networks presents significant challenges to network security due to their malicious activities. While graph neural network models have shown promise in detecting social bots, acquiring a large number of high-quality labeled accounts remains challenging, impacting bot detection performance. To address this issue, we introduce BotCL, a social bot detection model that employs contrastive learning through data augmentation. Initially, we build a directed graph based on following/follower relationships, utilizing semantic, attribute, and structural features of accounts as initial node features. We then simulate account behaviors within the social network and apply two data augmentation techniques to generate multiple views of the directed graph. Subsequently, we encode the generated views using relational graph convolutional networks, achieving maximum homogeneity in node representations by minimizing the contrastive loss. Finally, node labels are predicted using Softmax. The proposed method augments data based on its distribution, showcasing robustness to noise. Extensive experimental results on Cresci-2015, Twibot-20, and Twibot-22 datasets demonstrate that our approach surpasses the state-of-the-art methods in terms of performance.

关键词： Social bot detection graph contrastive learning Data augmentation relational graph convolutional network

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